Robotic Research Engineer

About Us

Trener 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.

Trener 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.

Our 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.

We 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.

This 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.

This 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.

You 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.

• Own day-to-day readiness of robot learning lab systems: robot arms, grippers, cameras, trackers, force/torque sensors, safety equipment, and compute boxes.
• Keep demos and data collection rigs operational, calibrated, documented, and ready for scheduled runs, including customer, investor, and partner demos at the San Jose office.
• Run and support teleop data collection using VR headsets, trackers, robot controllers, and recording tools.
• Support the UMI data collection by testing hardware, calibration, synchronization, ergonomics, and data quality in real collection sessions.
• Collect 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.
• Perform first-pass data QA: verify timestamps, camera views, robot state, action logs, task metadata, labels, and discard reasons before data enters the training dataset.
• Work with the data platform team to make sure collected episodes upload correctly and carry the metadata needed for training, evaluation, and model lineage.
• Work with robot learning engineers on what data is useful for VLA training, policy evaluation, recovery cases, and failure analysis.
• Work with systems and controls engineers to debug policy execution, behavior tree integration, robot motion, safety stops, and runtime logging.
• Design, fabricate, and maintain fixtures, mounts, adapters, test objects, and cell layouts for repeatable experiments.
• Document setup procedures, calibration steps, collection protocols, failure modes, and lab operating practices, and keep them in sync with the Trondheim lab.

• Hands-on experience building, operating, or maintaining robotic manipulation systems in a lab, company, or advanced academic setting.
• Strong practical understanding of robot arms, grippers, cameras, sensors, calibration, and safety around physical robots.
• Ability to debug real robot systems across hardware, software, networking, sensors, and operator workflow.
• Programming ability in Python; enough C++ or ROS 2 familiarity to work effectively with robotics software…