{"id":624147,"url":"https://alion.io/job/fieldai-senior-research-engineer-manipulation","title":"Senior Research Engineer, Humanoid Manipulation","company":{"id":672883,"name":"FieldAI","domain":"fieldai.com","url":"https://alion.io/company/fieldai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":81,"open_postings":104,"ghost_share":0.231,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":62,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Robotics","role_family":"Robotics","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Boston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Imitation Learning","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-09-02T19:34:09Z","employer_posted_date":"2026-09-02","last_verified_at":"2026-10-31T01:58:23Z","board_verified":true,"closed_at":null,"days_open":58,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":56},"description":"We are seeking a Research Engineer to join our manipulation efforts. In this role, you will work at the intersection of robotics research and applied engineering, building, training, testing, and refining large-scale learned manipulation models and capabilities that accelerate autonomous control and loco-manipulation on humanoid robots. You will collaborate closely with research scientists, engineers, and product partners to design novel manipulation strategies and deliver systems that directly feed into FieldAI’s robot learning pipelines.\nYou will also play a key role in advancing robotics foundation models designed to be generalizable across embodiments, with an initial focus on humanoid platforms. The work will emphasize combinations of learned and physically-grounded models, alongside the data and training systems required to scale them. This role is about pushing the frontier of manipulation research while ensuring that breakthroughs translate into practical, scalable autonomy in real-world environments.\nWhat You’ll Get To Do\nAdvance Humanoid Manipulation Research and Development\nDesign, implement, and evaluate learning-based manipulation models and strategies for humanoid robots across a wide range of tasks.\n\nDrive projects from early concepts through on-robot testing and deployment.\n\nDevelop loco-manipulation capabilities that integrate perception, control, and planning.\n\nDrive Robotics Foundation Model Development\nContribute to foundation models for manipulation, working on model architecture, data collection, large-scale training pipelines, and deployment infrastructure.\n\nEnsure model development supports generalization and transfer across diverse robotic platforms.\n\nCollaborate with research scientists to integrate large-scale manipulation data into learning pipelines powering foundation models.\n\nTrain and evaluate manipulation models using imitation learning, reinforcement learning, and other training approaches.\n\nBuild Systems That Bridge Research and Deployment\nTranslate research ideas into reliable robotic systems that operate in real-world conditions.\n\nEnsure systems are robust, reproducible, and aligned with data collection and learning objectives.\n\nDevelop experimental infrastructure to support rapid iteration, large-scale training, and evaluation.\n\nCollaborate Across Disciplines\nPartner with mechanical and electrical engineers on hardware integration and system bring-up.\n\nWork closely with field teams to refine interfaces and improve manipulation performance.\n\nAct as a connective layer between autonomy research and applied robotics engineering.\n\nRapidly Iterate and Deliver\nPrototype quickly, run experiments on hardware, and validate results in the field.\n\nBalance exploratory research with concrete deliverables that support near-term goals.\n\nDebug complex system-level issues spanning software, hardware, data, training infrastructure, and learning.\n\nWhat You Have\nBachelor’s, Master’s, or PhD in Robotics, Computer Science, Mechanical Engineering, or a related field.\n\n2+ years of hands-on experience in robotic manipulation and/or robot learning in academic or industry settings.\n\nStrong foundation in robot kinematics, dynamics, and control, with a focus on manipulation.\n\nStrong background in learning-based manipulation, with an emphasis on reinforcement learning and imitation learning, and experience training modern deep-learning models.\n\nExperience using frameworks such as PyTorch and building reproducible model-training and evaluation pipelines.\n\nProven experience implementing and evaluating robotic systems on real hardware.\n\nAbility to work effectively in fast-paced, highly collaborative environments.\n\nCuriosity, ownership, and the confidence to challenge assumptions and propose new approaches.\n\nThe Extras That Set You Apart\nExperience working with humanoid robots or multi-fingered robotic hands.\n\nExperience training large-scale robot foundation models.\n\nExperience with reinforcement-learning fine-tuning, offline RL, and related policy training methods.\n\nFamiliarity with large-scale data collection, spanning simulation, web-scale data, human-in-the-loop data, and autonomously-collected data.\n\nPublications or open-source contributions in top robotics or machine-learning venues.\n\nExperience with large-scale robotics data collection and dataset management.\n\nStrong interest in bridging cutting-edge research with field-ready robotic 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