Tech Lead, Robotic AI Model

What you'll do

• Build pipelines to curate robot demonstrations and fine-tune foundation models and policies using supervised learning, reinforcement learning, and preference optimization.
• Train and validate policies in simulation and on robots, including sim-to-real transfer and benchmarking.
• Optimize models for on-robot inference and integrate learned policies with robot software, controls, perception, and hardware teams.

What they ask for

• Master’s or PhD in Robotics, Computer Science, Machine Learning, or a closely related field.
• 3+ years of hands-on robot-learning experience, including imitation learning, behavior cloning, or visuomotor policy training on real or simulated robots.
• Deep expertise in at least one post-training paradigm: SFT on robot demonstrations, RL-based policy optimization, or diffusion/flow-matching policy training.
• Strong PyTorch and Python skills; experience training and debugging models at scale, practical robot simulation and sim-to-real experience, and familiarity with ROS/ROS2 and real-time control systems.

Nice to have

• Experience fine-tuning VLA models or working with real robot platforms and large-scale teleoperation data collection.
• Familiarity with RLHF/DPO/GRPO, robot-learning infrastructure, on-device optimization, or publications at top robotics or ML venues.

Pay

($150,000-$180,000 USD) base salary

Tools

PyTorch, FSDP, DeepSpeed, Isaac Sim, MuJoCo, PyBullet, SAPIEN, ROS/ROS2

AI focus

Robot foundation-model post-training, VLA and visuomotor policy fine-tuning, reinforcement learning, simulation-to-real transfer, and real-world robot deployment.