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.

  • Lawton, OK

  • 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.

  • Worcester, MA

  • 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.

  • Moscow, ID

  • 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.

  • Shreveport, LA

  • 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.

  • Springfield, IL

  • 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.

  • Buffalo, NY

  • no content

North America Summer 2027 Sales Internship

About Our Internship Program We offer world-class training, professional development and unlimited growth potential. Here, you won’t just grow your resume; you’ll develop invaluable skills to excel today and build a meaningful career for tomorrow. Our internship program allows you to get hands-on experience at a global, growing enterprise. While building your network, you will join global internship experience events, have opportunities to volunteer, hear from senior executives, and be mentored by leadership. Job Overview The sales internship is a comprehensive 10-week program in one of Gartner’s high performing sales channels. Interns work alongside a team of full-time sales associates, gaining hands-on training & exposure to both Business Development and Account Management for a comprehensive understanding of Gartner’s sales process. PLEASE NOTE: This is a 10-week internship taking place May 24 - July 30, 2027. You must be available to work the full 10 weeks from the aligned Gartner office location. What You Will Do Learn in a hands-on training environment about how to execute the top priorities of Gartner’s sales organization. Work with a Gartner sales team and their clients/prospects to understand best practices for engagement, retention, and growth. Participate in sales and retention activities alongside your aligned sales team. Gain real-time sales experience through shadowing sales associates and practicing different components of Gartner’s sales process. What You Will Get Extensive support: work closely with and learn from colleagues, mentors, and managers. Make an impact: the ability to do meaningful real-life work and learn executives’ priorities and opportunities. Meet great people: collaborate with like-minded, goal-oriented peers and mentors who are always there to help. Be empowered: join an organization that allows associates to reach their full potential in an environment where everyone has equitable access to opportunities. Have fun: socialize and build your network with other interns around the globe. Fuel your future: develop your skillset as you look ahead to your future career. What You Will Need Enrollment in a Bachelor’s degree program with expected graduation date within a year of successfully completing the internship program (December 2027 or May 2028 graduation date). Exceptional communication and time management skills. A results-based, goal-focused mindset with a natural curiosity, demonstrated by extracurricular involvement in clubs or community. Leadership and/or work experience to supplement strong student achievement. internships LI-AD8 Who are we? At Gartner, Inc. (NYSE:IT), we guide the leaders who shape the world. Our mission relies on expert analysis and bold ideas to deliver actionable, objective business and technology insights, helping enterprise leaders and their teams succeed with their mission-critical priorities. Since our founding in 1979, we’ve grown to 20,000 associates globally who support over 13,000 client enterprises in ~90 countries and territories. We do important, interesting and substantive work that matters. That’s why we hire associates with the intellectual curiosity, energy and drive to want to make a difference. The bar is unapologetically high. So is the impact you can have here. What makes Gartner a great place to work? Our vast, virtually untapped market potential offers limitless opportunities - opportunities that may not even exist right now - for you to grow professionally and flourish personally. How far you go is driven by your passion and performance. We hire remarkable people who collaborate and win as a team. Together, our singular, unifying goal is to deliver results for our clients. Our teams are inclusive and composed of individuals from different geographies, cultures, religions, ethnicities, races, genders, sexual orientations, abilities and generations. We invest in great leaders who bring out the best in you and the company, enabling us to multiply our impact and results. This is why, year after year, we are recognized worldwide as a great place to work. Gartner is the world authority on AI At Gartner, you’ll join a company at the very center of the AI revolution. Gartner has proactive, objective guidance throughout clients’ AI journeys. We set the standard for how organizations leverage artificial intelligence to drive meaningful impact. You’ll have access to unmatched resources, expertise, and technology, and play a key role in helping Gartner and our clients innovate and grow as we leverage AI to transform business and technology landscapes. It’s an exciting time to be at Gartner, with limitless opportunities to make a real impact, grow your skills, and build a lasting, meaningful career in a field that’s reshaping the way we operate. If you’re passionate about AI and want to be part of a team that’s guiding the leaders who shape the world, Gartner is the place for you. What do we offer? Gartner offers world-class benefits, highly competitive compensation and disproportionate rewards for top performers. In our hybrid work environment, we provide the flexibility and support for you to thrive - working virtually when it’s productive to do so and getting together with colleagues in a vibrant community that is purposeful, engaging and inspiring. Ready to grow your career with Gartner? Join us. Gartner believes in fair and equitable pay. A reasonable estimate of the hourly rate for this role is 17 USD - 25 USD. Please note that actual rates may vary within the range, or be above or below the range, based on factors including, but not limited to, education, training, experience, professional achievement, business need, and location. We also offer benefit programs including paid sick time off, and a 401k match for employees working 20 hours a week or more. The policy of Gartner is to provide equal employment opportunities to all applicants and employees without regard to race, color, creed, religion, sex, sexual orientation, gender identity, marital status, citizenship status, age, national origin, ancestry, disability, veteran status, or any other legally protected status and to seek to advance the principles of equal employment opportunity. Gartner is committed to being an Equal Opportunity Employer and offers opportunities to all job seekers, including job seekers with disabilities. If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access the Company’s career webpage as a result of your disability. You may request reasonable accommodations by calling Human Resources at 1 (203) 964-0096 or by sending an email to [email protected]. Job Requisition ID:112992 By submitting your information and application, you confirm that you have read and agree to the country or regional recruitment notice linked below applicable to your place of residence. Gartner Applicant Privacy Link: https://jobs.gartner.com/applicant-privacy-policy For efficient navigation through the application, please only use the back button within the application, not the back arrow within your browser.

  • Irving, TX

  • 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.

  • Valdosta, 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.

  • Evansville, IN

  • 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.

  • Grand Rapids, MI

  • 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.

  • Nacogdoches, TX

  • 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.

  • Jacksonville, FL

  • no content