{"schemaVersion":"jobsearcher.job.v1","id":"b4c8ce853f13fc1cf9a04d75","url":"https://jobsearcher.com/jobs/b4c8ce853f13fc1cf9a04d75","canonicalUrl":"https://jobsearcher.com/jobs/b4c8ce853f13fc1cf9a04d75","title":"AI Research Engineer, Reinforcement Learning","description":"AI Research Engineer, Reinforcement Learning | AI & Robotics\nLocation: Palo Alto, CA (on-site)\n\nAbout 1X\nWe build humanoid robots that work alongside people to solve labor shortages and create abundance.\n\nThe Role\nAs 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.\n\nYou Will\n\nOwn the full stack of engineering tasks, from data engineering and model architecture to product deployment\n\nTrain NEO on a variety of manipulation and locomotion tasks\n\nCollaborate with hardware teams to bridge the sim-to-real gap for policies trained in simulation\n\nPartner with controls, QA, and data collection teams to ship RL policies to production\n\nDeploy reinforcement learning-trained skills into real-world home environments\n\nMust Have\n\nStrong programming experience in Python and/or C++ with familiarity using build tools such as Bazel\n\nProficiency with PyTorch\n\nHands‑on experience with simulation platforms like Isaac Sim or MuJoCo\n\nExperience training reinforcement learning policies, especially for manipulation or locomotion\n\nAbility to collaborate cross-functionally with hardware, control, data, and QA teams\n\nDemonstrated experience addressing the sim-to-real gap\n\nBenefits & Compensation\n\nSalary Range: $180,000 – $250,000 + Equity\n\nHealth, dental, and vision insurance\n\n401(k) with company match\n\nPaid time off and holidays\n\nEqual Opportunity Employer\n1X 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.\n\n#J-18808-Ljbffr","company":"SupportFinity","rawCompany":"supportfinity","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-06-18T03:23:45.703Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"17-2199.08","title":"Robotics Engineers","slug":"robotics-engineers"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"AI Research Engineer, Reinforcement Learning","description":"AI Research Engineer, Reinforcement Learning | AI & Robotics\nLocation: Palo Alto, CA (on-site)\n\nAbout 1X\nWe build humanoid robots that work alongside people to solve labor shortages and create abundance.\n\nThe Role\nAs 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.\n\nYou Will\n\nOwn the full stack of engineering tasks, from data engineering and model architecture to product deployment\n\nTrain NEO on a variety of manipulation and locomotion tasks\n\nCollaborate with hardware teams to bridge the sim-to-real gap for policies trained in simulation\n\nPartner with controls, QA, and data collection teams to ship RL policies to production\n\nDeploy reinforcement learning-trained skills into real-world home environments\n\nMust Have\n\nStrong programming experience in Python and/or C++ with familiarity using build tools such as Bazel\n\nProficiency with PyTorch\n\nHands‑on experience with simulation platforms like Isaac Sim or MuJoCo\n\nExperience training reinforcement learning policies, especially for manipulation or locomotion\n\nAbility to collaborate cross-functionally with hardware, control, data, and QA teams\n\nDemonstrated experience addressing the sim-to-real gap\n\nBenefits & Compensation\n\nSalary Range: $180,000 – $250,000 + Equity\n\nHealth, dental, and vision insurance\n\n401(k) with company match\n\nPaid time off and holidays\n\nEqual Opportunity Employer\n1X 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.\n\n#J-18808-Ljbffr","datePosted":"2026-06-18T03:23:45.703Z","dateModified":"2026-06-18T03:23:45.703Z","hiringOrganization":{"@type":"Organization","name":"SupportFinity","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b4c8ce853f13fc1cf9a04d75"},"url":"https://jobsearcher.com/jobs/b4c8ce853f13fc1cf9a04d75"}}