1 month ago
San Jose, CA, USASenior
Base Salary
$150k - $300k/yr
Responsibilities
- Own and scale infrastructure for training reinforcement-learning whole-body control policies, including simulation, data pipelines, orchestration, and visualizations.
- Design fast, reliable, and configurable systems for controls engineers.
- Improve cluster utilization and minimize downtime to accelerate iteration cycles.
- Evaluate and integrate physics engines, simulation environments, and parameterizations.
- Optimize hyperparameters and infrastructure for training efficiency and model performance.
- Build tooling to move policies from training through validation and deployment on hardware.
Requirements
- Strong software engineering fundamentals and production experience with Python and PyTorch.
- Experience building or scaling training infrastructure for robotics, control systems, or large-scale machine learning workloads.
- Familiarity with NVIDIA PhysX, MuJoCo, Warp, or PyBullet.
- Working knowledge of dynamics, controls, and robotics systems.
- Experience with reinforcement learning, imitation learning, or policy distillation.
- Experience modeling contact interactions and photorealistic simulation environments for complex manipulation tasks.
- Bonus: experience with humanoid or legged robot control, distributed systems, job schedulers, cluster management, or deploying ML models or control policies to real-world systems.
Benefits
- Full-time position based in North San Jose, California, requiring five days per week in-office.
- Total compensation may include additional components or benefits depending on the role.
Categories
ML EngineeringRobotics