AI Research Engineer, Reinforcement Learning | AI & Robotics Location: Palo Alto, CA (on-site) About 1X We build humanoid robots that work alongside people to solve labor shortages and create abundance. The Role As a Research Engineer focused on Reinforcement Learning, you will be responsible for teaching NEO new capabilities through RL algorithms. You’ll work across both simulation and real-world environments to build robust behaviors and deploy them into homes. This role will be instrumental in making our robots safer, more capable, and increasingly versatile. You Will - Own the full stack of engineering tasks, from data engineering and model architecture to product deployment - Train NEO on a variety of manipulation and locomotion tasks - Collaborate with hardware teams to bridge the sim-to-real gap for policies trained in simulation - Partner with controls, QA, and data collection teams to ship RL policies to production - Deploy reinforcement learning-trained skills into real-world home environments Must Have - Strong programming experience in Python and/or C++ with familiarity using build tools such as Bazel - Proficiency with PyTorch - Hands-on experience with simulation platforms like Isaac Sim or MuJoCo - Experience training reinforcement learning policies, especially for manipulation or locomotion - Ability to collaborate cross-functionally with hardware, control, data, and QA teams - Demonstrated experience addressing the sim-to-real gap Benefits & Compensation - Salary Range: $180,000 – $250,000 + Equity - Health, dental, and vision insurance - 401(k) with company match - Paid time off and holidays Equal Opportunity Employer 1X is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, ancestry, citizenship, age, marital status, medical condition, genetic information, disability, military or veteran status, or any other characteristic protected under applicable federal, state, or local law.