{"id":1652871,"url":"https://alion.io/job/origin-founding-robot-learning-research-lead","title":"Founding Robot Learning Research Lead","company":{"id":686527,"name":"Origin","domain":"origin.tech","url":"https://alion.io/company/origin-7","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":100,"open_postings":23,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":48,"computed_at":"2026-10-20T05:45:00Z"}},"role":"Science & Research","role_family":"Science & Research","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":104000,"max_usd":275000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":584},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Embodied AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Isaac Lab","optional":false},{"name":"Isaac Sim","optional":false},{"name":"ONNX","optional":false},{"name":"OpenVLA","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"ROS","optional":false},{"name":"ROS2","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Teleoperation","optional":false},{"name":"TensorRT","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-10-19T11:52:50Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-20T16:49:12Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About Origin\nOrigin is building Physical AI for the built world - starting with autonomous robots for Interior Construction. We are building Construction Action Models which allows our modular robots to learn, adapt and work in unstructured construction job site.\nOur robots are already deployed on live sites in New York City, helping accelerate schedules for large-scale commercial projects while improving safety and predictability on the job site. Backed by Tier-1 investors, Origin is working to close the gap between America’s surging demand for housing, data centers, and manufacturing infrastructure, and the construction industry’s growing labor shortage.\nThe Role\nOur system runs a Multi Agent Action Expert architecture: classical precision algorithms orchestrated alongside learned policies. The job is systematically expanding the learned components while keeping the system production-safe. You own the full lifecycle of learned components on OG-1: from data collection and model training through edge deployment on Jetson AGX Orin. Every research project will have a deployment milestone. This is not a lab position.\nWhat you will own?\nDefine the technical roadmap for Robot Learning and Embodied AI.\nBuild and deploy learned policies for real-world mobile manipulation and contact-rich tasks.\nDevelop imitation learning, reinforcement learning, VLA, and learning-from-demonstration systems.\nFine-tune and adapt open-source VLA/foundation models for our robot platform.\nBuild scalable teleoperation → dataset → training → evaluation → deployment loops.\nDevelop DAgger / HG-DAgger and human-in-the-loop data collection pipelines.\nBuild simulation environments and training pipelines using NVIDIA Isaac Sim / Isaac Lab.\nDevelop sim-to-real strategies including domain randomization, system identification, and real-world policy adaptation.\nExplore world models and latent dynamics models for planning, prediction, and policy learning.\nIntegrate learned policies with our existing ROS2 perception, planning, manipulation, control, and safety stack.\nOptimize inference for deployment on edge GPUs using TensorRT, ONNX, CUDA, profiling, quantization, and related techniques.\nDebug policies on physical robots: latency, observation drift, calibration errors, distribution shift, contact instability, action representation, control frequency, and hardware-induced failures.\nEstablish rigorous evaluation for learned systems across simulation, replay datasets, and physical robot experiments.\nBuild and mentor the Robot Learning team as we scale.\nOur broader robot architecture already spans perception, manipulation, planning, ROS integration, simulation, force/impedance control, quality assessment, and autonomous recovery.\nRequirements\nBS/MS/PhD in CS, Robotics, ML, or related field from Stanford, MIT, UC Berkeley, CMU, Georgia Tech, ETH Zürich, or UPenn, or equivalent exceptional experience shipping learned systems on physical robots.\nPhD: minimum 2 years relevant experience. Without PhD: minimum 5 years relevant experience.\nStrong Python and PyTorch; comfortable modifying research codebases and open-source VLA implementations.\nExperience in at least two of: imitation learning, RL, VLA/VLMs, robot learning from demonstration, sim-to-real.\nTrack record deploying ML on real robots - not just training policies, but debugging why they fail on actual hardware.\nWorking knowledge of ROS2 or equivalent robotics middleware.\nExperience with simulation systems such as NVIDIA Isaac Sim / Isaac Lab.\nGPU inference profiling and optimization (TensorRT, ONNX, CUDA); understand the impact of policy latency on real-time robot control.\nStrong Plus\nHands-on with VLA architectures such as π0/π0.5, OpenVLA, RT-2, Octo, or robotics foundation-model fine-tuning.\nTeleoperation data collection and DAgger / HG-DAgger pipelines.\nWorld models such as DreamerV3, V-JEPA, or latent dynamics models.\nExperience with contact-rich manipulation, construction, manufacturing, or industrial robotics.\nPublications at CoRL, RSS, ICRA, NeurIPS - valued, but equivalent shipped work on real robots counts.","description_format":"text","description_chars":4099,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Construction Robotics","Robotic Software & Control Systems"],"lifecycle":[{"event":"open","at":"2026-10-02T00:46:26Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":48,"reasons":["conf:2","velocity","win:early"],"computed_at":"2026-10-20T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/origin-founding-robot-learning-research-lead","json_url":"https://alion.io/job/origin-founding-robot-learning-research-lead.json","meta":{"generated_at":"2026-10-20T20:25:10Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":23643,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-21T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":686527},"rest":"https://alion.io/mcp/rest/get_company?id=686527"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Forigin-founding-robot-learning-research-lead"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Forigin-founding-robot-learning-research-lead"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Forigin-founding-robot-learning-research-lead"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/origin-founding-robot-learning-research-lead\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Forigin-founding-robot-learning-research-lead"}]}