Finance

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Augusta, GA

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Pensacola, FL

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Carson City, NV

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Jefferson City, MO

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Vestal, NY

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Saint Louis, MO

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Chesapeake, VA

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Bowie, MD

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Lewiston, ME

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Rochester, NH

  • no content

Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Team: Apollo - Block Applied R&D Location: Remote (US / Canada) Duration: Fall/Winter 2026 co-op - 8 months, flexible start September 2026 Level: Graduate student (MS or PhD, returning to your program after the co-op) About Apollo Apollo leads Block's efforts to build the Customer World Model (CWM) : a continuously evolving representation of each customer's goals, context, history, constraints, and likely future needs. The CWM powers proactive intelligence across Block's ecosystem. Instead of customers navigating products in search of features, intelligence observes their world, understands what matters, anticipates what comes next, and initiates actions on their behalf. We believe the next generation of AI products will not be defined by chat interfaces or isolated agents. They will be defined by rich world models that enable systems to reason over a customer's evolving state, make better decisions, and learn continuously from outcomes. Apollo designs, prototypes, and guides the development of this intelligence layer. About the role We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year. What you'll work on Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas: Customer World Models Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data. Examples include: Representation learning over long-horizon customer histories Event-based foundation models Multi-modal customer representations spanning structured, sequential, and graph data Memory architectures for long-term customer understanding Proactive Intelligence Developing systems that can anticipate customer needs and initiate helpful actions before being asked. Examples include: Opportunity detection and next-best-action systems Long-horizon planning and decision-making Preference and goal inference Learning when intervention creates value versus friction Agentic Decision Systems Building agents that reason over customer world models and take actions in real environments. Examples include: Tool use and planning Multi-step reasoning over customer state Autonomous workflow execution Recovery and adaptation under uncertainty Learning from Feedback Loops Developing methods that allow intelligence to improve continuously from real-world outcomes. Examples include: Reinforcement learning from customer and product feedback Reward modeling and preference learning Counterfactual evaluation Credit assignment over long decision horizons Evaluation and Measurement Building evaluation frameworks that predict real-world performance, trust, and customer value. Examples include: Simulated customer environments Longitudinal evaluation Decision quality metrics Safety and reliability benchmarks What we're looking for We're looking for researchers interested in building systems that understand people, learn from experience, and improve over time. Required Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op. Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models. Experience conducting independent research and translating ideas into working systems. Fluency in Python and experience with PyTorch, JAX, or similar frameworks. Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work. Nice to have Experience with large language models and agentic systems. Experience with reinforcement learning, reward modeling, or sequential decision-making. Experience with representation learning for structured, temporal, or graph data. Familiarity with large-scale training and production ML systems. Interest in building AI systems that directly affect customer outcomes. What you'll get Direct mentorship from researchers working on the future of proactive intelligence at Block. Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources. Opportunities to publish and contribute to open-source projects. A chance to shape foundational technology that could power the next generation of Block products. Exposure to both scientific research and product deployment, with a clear path from idea to impact. Application Guidelines Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed. Use of AI in Our Hiring Process We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws. Contact us here with hiring practice or data usage questions. Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. Check out our other benefits at Block. Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we're helping build a financial system that is open to everyone.

  • Kearney, NE

  • no content

Spring 2027 Electrical Engineering Co-op - Multiple Positions Available!

Company Summary Statement Louisville Gas and Electric Company and Kentucky Utilities Company, part of the PPL Corporation (NYSE: PPL) family of companies, are regulated utilities that serve more than 1.3 million customers and have consistently ranked among the best companies for customer service in the United States. LG&E serves 334,000 natural gas and 433,000 electric customers in Louisville and 16 surrounding counties. KU serves 569,000 customers in 77 Kentucky counties and five counties in Virginia. LG&E and KU are major employers and active supporters of the communities they serve. They empower employees, community members and initiatives across their service territory through volunteerism and investments in organizations that support education, sustainability and wellbeing. Overview Why Choose LG &E and KU for Your Electrical Engineering Co-op? At LG&E and KU, our engineering co-op and internship program offers a dynamic and engaging environment where students can apply classroom knowledge to real-world challenges. As part of our robust engineering workforce, you'll gain hands-on experience in a variety of disciplines that require analytical thinking, problem-solving, and innovation. We take pride in our collaborative culture and strong commitment to serving our communities. Our employees are united by a shared purpose: delivering safe, reliable energy to our customers every day. Responsibilities What You'll Do As an engineering co-op or intern, you will work alongside experienced professionals and contribute to meaningful projects that support our operations. Your responsibilities may include: Identifying opportunities for process improvement and recommending solutions. Collecting, analyzing, and interpreting data using established engineering procedures. Performing routine engineering tasks such as calculations, testing, and analysis. Supporting operations and maintenance teams with troubleshooting and technical evaluations. Assisting engineers with large-scale projects and initiatives. Participating in the development of engineering goals, budgets, and procedures. Promoting a safe and environmentally responsible workplace. Reviewing assignments for efficiency and cost-effectiveness. Performing other duties as assigned by management. What You'll Gain Exposure to practical engineering applications in the utility industry. Mentorship from experienced engineers. Opportunities to contribute to impactful projects. Insight into utility operations, safety standards, and environmental regulations. Openings Available in the following Departments: Cane Run, Mill Creek and Trimble County Generating Stations - Power Plant Operations In Power Plant Operations, you'll support the daily operation and maintenance of our power generating stations. You'll apply analytical skills to investigate root cause failures and recommend corrective actions. This role also involves working with experienced engineers on projects that will improve system reliability, and engineering design solutions that directly impact plant performance and safety. Fleet Engineering As a co-op in Fleet Engineering , you'll gain a comprehensive understanding of power plant operations through guided plant tours and hands-on study of generating equipment. You'll learn the fundamentals of project planning and management, and apply your skills by visiting various plants to investigate operational issues. You'll be responsible for analyzing problems and presenting engineering-based solutions to both plant and engineering teams. This role offers a balanced experience between office-based planning and fieldwork at generation sites. Gas Transmission Integrity & Compliance: Learning engineering specifications, construction standards, and inspection practices. GIS and document management system knowledge developed. Conduct quality assurance reviews of construction records and compile engineering data in GIS. Operations Center Distribution Engineering As a co-op in Operations Center Distribution Engineering , you'll progressively take on responsibilities related to the design, construction, maintenance, and protection of the electrical distribution system. You'll gain hands-on experience with both overhead and underground systems, overcurrent protection, line capacitor installation, and other power quality and service reliability initiatives. This role offers a balanced mix of fieldwork and computer-based applications, providing a well-rounded understanding of distribution engineering practices Project Engineering As a co-op in Project Engineering , you will assist with large-scale construction and design initiatives across our facilities. You'll gather technical data, coordinate with vendors, and track project milestones. As your term progresses, you may take ownership of smaller contracts or projects, gaining valuable experience in project management. Most roles in this department are based full-time at active project sites, offering immersive, hands-on learning. SCADA & Automation As a co-op in SCADA & Automation, you'll support the design and implementation of control and automation systems that help operate and monitor the transmission network. You'll gain experience programming and configuring devices such as Remote Terminal Units (RTUs), enabling communication between substation equipment and the control center. You'll also assist with automation initiatives that improve operational efficiency, event monitoring, and data management to support regulatory compliance and system reliability. This role offers valuable experience in power system operations, automation, communications, and technology that helps keep the electric grid reliable and secure. Transmission Investment Planning: The co-op will assist in analyzing and organizing data, focusing on asset nameplate information and asset health data. The co-op will learn how to review as-built drawings and comparing to digital records, assist with monthly performance reporting and review and analyze algorithms related to asset health to ensure accuracy and reliability; improve the quality of data. The co-op will also assist with development of need statements and project proposals for upcoming initiatives, and create and maintain spreadsheets, databases and dashboards to accommodate data requests and enhance data visualization. Transmission Reliability Performance and Standards As a co-op in Transmission Reliability Performance and Standards, you'll assist in managing key databases related to facility ratings and system events, encompassing data from Line, Substation, Protection, Operations, and Planning teams. Your assignments may include data entry and analysis, outage and performance metrics reporting, software screen creation, and testing. Through this role, you'll develop skills in system modeling, database configuration, and the use of various reporting tools-gaining valuable insight into the reliability and compliance aspects of transmission operations. Transmission Strategy & Planning As a co-op in Transmission Strategy & Planning, you'll work with Siemens PSS®E and PSS®D software to perform dynamic and stability simulations of electrical faults on the transmission system. You'll compare simulation results against system criteria, assist with reviewing Python code used in modeling, and analyze model data within PSS®E cases. Additional responsibilities may include editing dynamic data in simulation databases and drafting technical reports to summarize findings. This role offers a unique blend of power system analysis, programming, and strategic planning. Qualifications Required Education/Experience Available full time during the Spring 2027 semester (January - May) Pursuing an ABET Accredited Bachelor's degree with declared major in Electrical Engineering Minimum 2.50 GPA LG&EKU INDLGE

  • Louisville, KY

  • no content