Robot Learning Engineer
Build and deploy learned components for contact-rich manipulation on physical robots, owning data collection, model training, evaluation, and edge deployment. The role requires strong Python and PyTorch skills, robotics middleware and simulation experience, and hands-on machine-learning deployment on real hardware.
About the job
Responsibilities
- Train and deploy vision-language-action (VLA) models for contact-rich manipulation using imitation-learning infrastructure.
- Build data-collection pipelines, including teleoperation with GELLO, SpaceMouse, and VR; DAgger-style online correction; and demonstration curation.
- Research and prototype world models for surface-state prediction, spray dynamics, and anomaly detection.
- Design offline evaluation metrics that predict real-world finishing quality before deployment.
- Optimize models for edge deployment through TensorRT compilation, latency profiling, and memory budgeting on dual Jetson AGX Orin systems.
- Design interfaces where learned policies propose actions and deterministic safety layers enforce constraints.
- Own the lifecycle of learned components from data collection and model training through deployment on Jetson AGX Orin.
Requirements
- Bachelor's, master's, or PhD in computer science, robotics, machine learning, or equivalent experience shipping learned systems on physical robots.
- Strong Python and PyTorch skills, with comfort modifying research codebases and open-source VLA implementations.
- Experience in at least two of imitation learning, reinforcement learning, vision-language models, robot learning from demonstration, and sim-to-real.
- Track record deploying machine learning on real hardware and debugging policy failures on the actual robot.
- Working knowledge of ROS2 or equivalent robotics middleware.
- Experience with simulation systems such as Isaac Sim.
- GPU profiling and optimization experience with TensorRT, ONNX, and CUDA.
- Hands-on experience with world models, including predictive or generative environment models such as latent dynamics, video prediction, or planning-oriented models.
Nice-to-haves
- Hands-on experience with VLA architectures such as π0/π0.5, OpenVLA, RT-2, or Octo, or with foundation-model fine-tuning for robotics.
- Experience building teleoperation data-collection and DAgger/HG-DAgger pipelines.
- Experience with world-model architectures such as DreamerV3, V-JEPA, or latent dynamics models.
- Experience in construction, manufacturing, or contact-rich industrial domains.
- Publications at CoRL, RSS, ICRA, or NeurIPS; equivalent shipped work is valued.
Compensation
- The company is ideally seeking candidates with 3–4 years of hands-on experience in robotics, machine learning, or applied AI systems.
Skills
Python, PyTorch, Imitation Learning, Reinforcement Learning, Vision-Language Models, Robot Learning, Sim-To-Real, Ros2, Isaac Sim, TensorRT, Onnx, CUDA, Jetson Agx Orin, World Models, Teleoperation
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