Finance

Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale Write reliable, well-tested Python infrastructure rather than one-off research scripts Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones

  • Arlington, TX

  • no content

Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale Write reliable, well-tested Python infrastructure rather than one-off research scripts Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones

  • Frankfort, KY

  • no content

Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale Write reliable, well-tested Python infrastructure rather than one-off research scripts Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones

  • Vestal, NY

  • no content

Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale Write reliable, well-tested Python infrastructure rather than one-off research scripts Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones

  • Gaithersburg, MD

  • no content

Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale Write reliable, well-tested Python infrastructure rather than one-off research scripts Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones

  • Hagerstown, MD

  • no content

Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale Write reliable, well-tested Python infrastructure rather than one-off research scripts Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones

  • Meridian, ID

  • no content

Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale Write reliable, well-tested Python infrastructure rather than one-off research scripts Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones

  • Shreveport, LA

  • no content

Operational Leadership Rotation Program (Level 4)

L3Harris is dedicated to recruiting and developing high-performing talent who are passionate about what they do. Our employees are unified in a shared dedication to our customers' mission and quest for professional growth. L3Harris provides an inclusive, engaging environment designed to empower employees and promote work-life success. Fundamental to our culture is an unwavering focus on values, dedication to our communities, and commitment to excellence in everything we do. L3Harris is the Trusted Disruptor in defense tech. With customers' mission-critical needs always in mind, our employees deliver end-to-end technology solutions connecting the space, air, land, sea and cyber domains in the interest of national security. Job Title: Operational Leadership Rotation Program Job Code: 43162 Job Location: Huntsville, AL or Camden, AR or Orange, VA or Canoga Park, CA or Orlando, FL, Cincinnati, OH or Mason, OH Job Schedule: Varies by location Job Description: The Operations Leadership Rotation Program (OLRP) is a two-year learning opportunity for high performing individuals who aspire to become future Operations leaders. The OLRP provides abroad, cross-functional, hands-on learning experience which consists of four 6-month assignments that will stretch knowledge and capabilities across multiple Missle Solution (MSL) sector sites in the following Operations Centers of Excellence: Manufacturing Operations, Manufacturing Engineering, Quality & Mission Assurance, Scheduling & Material Planning and Supply Chain Material Management. The OLRP provides a great opportunity to gain a greater understanding of our business and develop leadership skills to prepare for future leadership roles. Essential Functions: Gain knowledge of operations functional areas to gain a greater understanding of Operations in the MSL Sector. Engage as a team member and contribute to each Ops COE rotation. Take on new/special assignments to help improve how we do business. Lead strategic initiatives as opportunities arise. Develop and demonstrate individual leadership skills. Must be willing accept temporary assignments in alternate locations every six months (Canoga Park, CA, Orlando, FL, Cincinnati, OH, Mason, OH, Camden, AR, Huntsville, AL & Orange, VA). Aspires to become an Operations Leader (Mfg Ops, Mfg Eng, QMA, SMP & SC). Qualifications: Bachelor's with 6 years prior experience in operations, Graduate Degree with 2 years prior experience in operations. In lieu of a degree, minimum of 10 years of prior related experience in operations. Preferred Additional Skills: Master's Degree preferred. Learning Agility-ability to ramp-up quickly, generalize knowledge and apply learning in new settings. In compliance with pay transparency requirements, the salary range for this role in the States of Ohio and Virginia is $69,500 - $129,500. For the State of California the salary range for this role is $79,000 - $147,000. This is not a guarantee of compensation or salary, as final offer amount may vary based on factors including but not limited to experience and geographic location. L3Harris also offers a variety of benefits, including health and disability insurance, 401(k) match, flexible spending accounts, EAP, education assistance, parental leave, paid time off, and company-paid holidays. The specific programs and options available to an employee may vary depending on date of hire, schedule type, and the applicability of collective bargaining agreements. LI-AT4 L3Harris Technologies is proud to be an Equal Opportunity Employer. L3Harris is committed to treating all employees and applicants for employment with respect and dignity and maintaining a workplace that is free from unlawful discrimination. All applicants will be considered for employment without regard to race, color, religion, age, national origin, ancestry, ethnicity, gender (including pregnancy, childbirth, breastfeeding or other related medical conditions), gender identity, gender expression, sexual orientation, marital status, veteran status, disability, genetic information, citizenship status, characteristic or membership in any other group protected by federal, state or local laws. L3Harris maintains a drug-free workplace and performs pre-employment substance abuse testing and background checks, where permitted by law. Please be aware many of our positions require the ability to obtain a security clearance. Security clearances may only be granted to U.S. citizens. In addition, applicants who accept a conditional offer of employment may be subject to government security investigation(s) and must meet eligibility requirements for access to classified information. By submitting your resume for this position, you understand and agree that L3Harris Technologies may share your resume, as well as any other related personal information or documentation you provide, with its subsidiaries and affiliated companies for the purpose of considering you for other available positions. L3Harris Technologies is an E-Verify Employer. Please click here for the E-Verify Poster in English or Spanish . For information regarding your Right To Work, please click here for English or Spanish .

  • Orlando, FL

  • no content

Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale Write reliable, well-tested Python infrastructure rather than one-off research scripts Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones

  • Indianapolis, IN

  • no content

Enterprise Client Operations Analyst [2027 EDGE Program]

JOB DESCRIPTION Make an impact at DTCC As an Enterprise Client Operations Analyst within Enterprise Services, you will support day-to-day processing activities that contribute to DTCC’s core securities processing and client services. Enterprise Client Operations delivers safety and soundness for the global financial services industry in an environment focused on minimizing risk and cost while maximizing efficiency. Your specific responsibilities will depend on the department you are assigned to, while providing practical exposure to operations and the role your team plays within Enterprise Client Operations. At DTCC, we help power the global financial markets by providing critical infrastructure that supports the safe and efficient processing of securities transactions. In this role, you will learn how your business area contributes to that broader mission while building practical skills through meaningful work, coaching, and structured development. About the EDGE Program EDGE is DTCC’s 18-month early-career development program for recent graduates. The program combines hands-on business experience, structured learning, mentorship, networking, and regular feedback to help analysts build technical, functional, and leadership capabilities. What you will gain A structured onboarding experience, including Welcome Week and program orientation. Hands-on experience through meaningful work aligned to business priorities. Exposure across the team through projects, shadowing, and stretch assignments. Monthly learning sessions, business education, professional development, and opportunities to hear from leaders and subject matter experts. Regular feedback, including a 90-day feedback and assessment milestone, manager coaching, and program check-ins. Mentorship, networking, and opportunities to build relationships across DTCC. What you will do Assist with day-to-day processing activities within Enterprise Client Operations. Participate in team meetings and build an understanding of the assigned department and the business role it plays within Enterprise Client Operations. Support team members with ad-hoc tasks, assignments, and special projects. Demonstrate technical knowledge through clear verbal and written communication. Build working relationships across multiple levels of management within Enterprise Client Operations. Align risk and control processes with day-to-day responsibilities to monitor and mitigate risk, escalating appropriately. What will help you succeed Required Pursuing a bachelor’s degree in Business, Economics, Finance, Accounting, or a related field. Expected graduation date: May 2027 Minimum GPA: 3.2 Authorization to work permanently in the United States without current or future sponsorship. Preferred Internship, project, research, student organization, volunteer, or leadership experience related to the role. Interest in financial services and how business, technology, operations, risk, and regulation connect. Proficient computer and technical skills. What makes you successful Curiosity and a willingness to learn in a complex industry. Strong analytical thinking, problem-solving, and attention to detail. Clear written and verbal communication. Collaboration, accountability, adaptability, and follow-through. Ability to ask thoughtful questions, use feedback, and connect your work to team goals. Benefits and work model Competitive compensation, including base pay and annual incentive opportunities, where applicable. Comprehensive health, wellness, retirement, and paid time off benefits based on location and eligibility. Hybrid work model: 3 days in office and 2 days remote, based on team schedule. The salary range is indicative for roles at the same level within DTCC across all US locations. Actual salary is determined based on the role, location, individual experience, skills, and other considerations. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

  • Jersey City, NJ

  • no content

Long-Horizon Coding Task Expert

Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale Write reliable, well-tested Python infrastructure rather than one-off research scripts Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones

  • Chesapeake, VA

  • no content

AI Agent Trajectory Annotator and Reviewer

Type: Contract, hourly Location: Remote Hours: 20–30 per week Pay: $20–30/hour, based on experience and language coverage Start: Immediate ABOUT THE ROLE We evaluate how well advanced AI coding agents solve real engineering problems. An agent is given a real open source codebase inside a container and a hard task, then works on its own for 80 to 250 steps. A trajectory is the full record of that run — every command, result, and decision. You will do two jobs, and you should expect either on any given day. •Annotate — Read a trajectory nobody has looked at yet and judge it step by step. • Review — Take an existing annotation, written by our AI tooling or another person, and confirm, correct, or reject it. TASKS YOU'LL SEE • Feature build — Add a working feature to a live library without breaking anything that already worked. • Rebuild — Work out what a compiled tool does by running it, then rebuild it to match its output, exit codes, and file effects. • Bug hunt — Find and fix twenty undocumented bugs across a dozen files with no test suite, then record what caused them. Mostly Python and Go, with some Rust, C, and Ct. A trajectory runs about 100 steps. WHAT YOU JUDGE IN A TRAJECTORY • Was the command right for the state the environment was actually in? • Did the agent read the previous output correctly? • Was the step wrong, or only inefficient — these are scored differently. • Where did the run first go off course — usually earlier than where it visibly broke. • Did the agent notice its own mistake and recover, or keep building on a false assumption? • Did it game the grader instead of solving the task (e.g., weakening a test or hardcoding an expected value)? WHAT WE NEED FROM YOU • Experience — 2 years in software engineering, DevOps, or site reliability, with real debugging in real codebases. • Languages — Strong in Python or Go, and able to read a language you've never used. • Linux — Comfortable with logs, running processes, build failures, and containers. • Workflow — Everyday Git, diffs, pull requests, and issue tracking. • Debugging — Able to work with no test suite and no error message pointing at the cause. • Focus — Able to hold context across a long run, because step 74 can depend on step 12. • Writing — Clear English, since every judgement needs an explanation another engineer can check.

  • Hattiesburg, MS

  • no content