{"id":200132,"url":"https://alion.io/job/menlo-research-researcher-manipulation","title":"Researcher, Manipulation","company":{"id":19297,"name":"Menlo Research","domain":"menlo.ai","url":"https://alion.io/company/menlo","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Robotics","role_family":"Robotics","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore","Palo Alto, United States"],"countries":["SG","US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"MuJoCo","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"ROS2","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Imitation Learning","optional":true},{"name":"Teleoperation","optional":true}],"status":"live","first_seen_at":"2026-09-04T08:49:57Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-10-24T18:43:30Z","board_verified":true,"closed_at":null,"days_open":50,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":50},"description":"About Menlo\nMenlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable, turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.\nThe Role\nWe are looking for a Researcher, Manipulation to give Asimov hands that can do real work. We recently ported our RL stack from locomotion to manipulation, taking Asimov from walking to flipping, and you will drive what comes next: grasping, dexterous and contact-rich tasks, and the kind of whole-body coordination where an arm, a torso, and two legs have to solve one problem together. You will get skills onto real hardware and keep pushing until they hold up outside a controlled setup.\nWhat You'll Do\nDesign, train, and ship reinforcement learning policies for manipulation, grasping, and dexterous, contact-rich tasks on Asimov\n\nBuild on our locomotion-to-manipulation RL work and extend it toward whole-body manipulation, where balance and reaching are one problem\n\nOwn the sim2real pipeline for manipulation, using our zero-shot sim2real approach so skills transfer to hardware cleanly\n\nDesign rewards, training environments, and randomization strategies in MuJoCo for contact-heavy tasks that are hard to simulate well\n\nClose the loop with real telemetry from real arms and hands, then feed failures back into the next policy\n\nCollaborate closely with locomotion, controls, and hardware researchers, since a humanoid manipulates with its whole body\n\nOpen-source what you can and share what you learn with the community building on Asimov\n\nWhat We Look For\nDeep hands-on experience with reinforcement learning or learning-based methods for manipulation, grasping, or dexterous control\n\nA track record of getting manipulation policies onto real robots, not only into papers or simulators\n\nStrong command of a physics simulator such as MuJoCo, Isaac, or similar, including contact modeling, reward shaping, and domain randomization\n\nFluency in Python and modern RL tooling, and comfort in a ROS2-based control stack\n\nA bias for shipping: you would rather watch a policy fumble a real object this week than perfect it in sim next month\n\nClear thinking about why contact-rich tasks fail, and how to close the gap\n\nNice to Have\nPublished work in manipulation, dexterous control, grasping, or imitation learning\n\nExperience with tactile sensing, force control, or teleoperation and demonstration data\n\nExperience with whole-body control that spans locomotion and manipulation\n\nContributions to open-source robotics or RL projects\n\nWhy Join Menlo\nYou will own manipulation for a humanoid that thousands of developers are building on in the open. Your skills will ship to real robots and real hands, not slides, and the community will see them work. If you want your research to reach into the physical world and pick things up, this is the place.\nA Note on AI\nYou don't need deep AI expertise for every role, but we do expect everyone at Menlo to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement. When that's the case, we'll say so explicitly in the qualifications. People who thrive here don't treat AI as a novelty. They use it to think better, and make their work easier for others to build on.\nEqual Opportunity and Accommodations\nWe hire talented people from a wide range of backgrounds. If you're excited about a role but don't meet every bullet, we still encourage you to apply. Menlo Research is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Menlo provides reasonable accommodations during the application process. 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