{"id":704701,"url":"https://alion.io/job/trener-robotic-research-engineer","title":"Robotic Research Engineer","company":{"id":685055,"name":"Trener Robotics, Inc.","domain":"trener.ai","url":"https://alion.io/company/trener","size_band":null,"is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"hiring_geo_confidence":"explicit","locations":["San Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":146000,"max_usd":316000,"period":"year","method":"role_country_seniority_unknown","sample_n":2391},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Behavior Tree","optional":false},{"name":"C++","optional":false},{"name":"Fusion 360","optional":false},{"name":"Imitation Learning","optional":false},{"name":"LeRobot","optional":false},{"name":"OPC UA","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"ROS","optional":false},{"name":"SolidWorks","optional":false},{"name":"Vision-Language-Action","optional":false}],"status":"live","first_seen_at":"2026-09-04T20:00:00Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-09-23T23:03:01Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"description":"About Us\nTrener Robotics is building the software stack that lets industrial robots become easier to deploy, operate, and improve over time. T-Labs is our robot-learning team, focused on training and integrating Vision-Language-Action models and learned manipulation policies for real industrial tasks. The team is split between our headquarters in San Jose, California and our lab in Trondheim, Norway.\nOur goal is general-purpose manipulation for industrial robots, starting with machine-tending part handling: collecting high-quality robot data across a growing range of tasks, training VLA policies from strong open backbones, and deploying learned skills into Acteris, our edge runtime for robot operation.\nAbout the Role\nWe are looking for a Robotics Research Engineer to own the physical learning loop in our San Jose lab. You will keep the robot systems, demos, teleop rigs, UMI data collection setups, cameras, grippers, and sensors running so the team can collect useful data and evaluate learned robot policies on real hardware.\nThis is not a pure lab manager role and not a pure technician role. You should be hands-on enough to wire, calibrate, fixture, debug, and operate robot cells, but technical enough to work closely with robot learning engineers, systems engineers, and controls engineers. Your job is to make sure experiments happen, demos stay ready, and the data that reaches the training pipeline is usable.\nThis is also not a pure ML job, but ML experience is required. You should be able to start training runs, run policy evaluations on the collected data and on the robot, and read the results well enough to tell the team whether the data or the policy is the problem.\nYou will be the on-site owner of the San Jose robot cells and will coordinate closely with the Trondheim lab so that both sites run the same collection protocols, calibration procedures, and data formats.\nHow You'll Move the Mission Forward\nOwn day-to-day readiness of robot learning lab systems: robot arms, grippers, cameras, trackers, force/torque sensors, safety equipment, and compute boxes.\n\nKeep demos and data collection rigs operational, calibrated, documented, and ready for scheduled runs, including customer, investor, and partner demos at the San Jose office.\n\nRun and support teleop data collection using VR headsets, trackers, robot controllers, and recording tools.\n\nSupport the UMI data collection by testing hardware, calibration, synchronization, ergonomics, and data quality in real collection sessions.\n\nCollect high-quality robot demonstrations and autonomous execution logs across a broad and growing range of manipulation tasks, from machine-tending part handling to general pick, place, insertion, and reorientation.\n\nPerform first-pass data QA: verify timestamps, camera views, robot state, action logs, task metadata, labels, and discard reasons before data enters the training dataset.\n\nWork with the data platform team to make sure collected episodes upload correctly and carry the metadata needed for training, evaluation, and model lineage.\n\nWork with robot learning engineers on what data is useful for VLA training, policy evaluation, recovery cases, and failure analysis.\n\nWork with systems and controls engineers to debug policy execution, behavior tree integration, robot motion, safety stops, and runtime logging.\n\nDesign, fabricate, and maintain fixtures, mounts, adapters, test objects, and cell layouts for repeatable experiments.\n\nDocument setup procedures, calibration steps, collection protocols, failure modes, and lab operating practices, and keep them in sync with the Trondheim lab.\n\nWhat You Need to Succeed\nHands-on experience building, operating, or maintaining robotic manipulation systems in a lab, company, or advanced academic setting.\n\nStrong practical understanding of robot arms, grippers, cameras, sensors, calibration, and safety around physical robots.\n\nAbility to debug real robot systems across hardware, software, networking, sensors, and operator workflow.\n\nProgramming ability in Python; enough C++ or ROS 2 familiarity to work effectively with robotics software engineers.\n\nComfort with data collection for robot learning: demonstrations, episodes, timestamps, camera streams, robot state, actions, labels, and quality checks.\n\nWorking ML experience: able to launch training runs from an existing pipeline, run evaluations, and interpret basic training and eval metrics.\n\nPractical mechanical/electrical skills: wiring, mounting, 3D printing, fixture setup, connector troubleshooting, and basic CAD.\n\nClear documentation habits and the discipline to keep lab setups repeatable.\n\nBusiness fluency in English.\n\nAuthorization to work in the United States.\n\nWhat Will Differentiate You\nExperience collecting data for imitation learning, diffusion policies, VLA policies, ACT, or similar robot learning methods.\n\nExperience with teleop systems, VR controllers, motion trackers, UMI-style collection rigs, or learning-from-demonstration workflows.\n\nHands-on experience with industrial or collaborative robots such as Universal Robots, ABB, FANUC, KUKA, or similar.\n\nExperience with camera calibration, multi-camera recording, depth cameras, wrist cameras, or vision-system debugging.\n\nFamiliarity with PyTorch, LeRobot-style datasets, RLDS, HDF5, Parquet, or other robot-learning data formats.\n\nExperience deploying or evaluating learned policies on real manipulators.\n\nExperience with industrial communication protocols such as Ethernet/IP, Modbus, OPC-UA, or vendor robot APIs.\n\nExperience with CAD tools such as SolidWorks, Fusion 360, or Onshape, plus 3D printing or basic machining.\n\nExperience working with a distributed team across time zones.\n\nWhy Join Us\nYou will own the lab systems that make robot learning real. When the team needs better data, a demo-ready cell, a calibrated teleop setup, or a policy tested on hardware, you are the person who makes it happen.\nThis role sits at the center of T-Labs' next six months: manipulation data collection, VLA model training, UMI validation, data pipeline flow, and Acteris integration. Your work will directly determine how fast the team can turn robot data into deployable learned skills.","description_format":"text","description_chars":6204,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"Advanced (C1)","optional":false}]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotics","Industrial Robotics"],"lifecycle":[{"event":"open","at":"2026-09-11T03:41:43Z"}],"liveness":{"score":46,"band":"ok","label":"Likely open","p_open":1,"p_active":0.62,"p_room":0.75,"age_days":18,"expected_fill_days":21,"reasons":["conf:0","win:late"],"computed_at":"2026-09-23T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/trener-robotic-research-engineer","json_url":"https://alion.io/job/trener-robotic-research-engineer.json","meta":{"generated_at":"2026-09-23T23:41:48Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}