The Optimus Simulation team at Tesla is looking to advance humanoid robotics by building a high-fidelity virtual world where Optimus can safely learn, adapt, and improve, and is seeking an engineer to help close the gap between simulation and reality.
Requirements
- Hands-on experience building and scaling distributed systems for large-scale computations
- Strong background in ML infrastructure, including designing training pipelines, data orchestration, and deployment of RL models at scale
- Proficiency in GPU optimizations for either inference or rendering
- Proficiency in Python, with familiarity in frameworks like PyTorch, TensorFlow, or RL libraries (e.g., Stable Baselines, RLlib), and a proven ability to write clean, scalable, and efficient code
- Ability to research, implement, and adapt cutting-edge techniques from academic and industry sources into practical, production-ready solutions for scalable RL in simulation
Responsibilities
- Design, implement, and optimize scalable Simulation and RL infrastructure for training humanoid robots in simulated environments, leveraging distributed systems for parallel processing and high-throughput simulations
- Optimize performance across the simulation stack, including distributed systems, Inference, and rendering, to ensure optimal usage of hardware resources and fast, efficient simulations
- Drive innovation in sim-to-real strategies using simulation technologies and distributed computing to ensure Optimus performs reliably across both virtual and real-world environments
- Deliver high-quality, production-ready code in a dynamic and fast-paced environment
Other
- Bachelor's, Master's, or Ph.D. degree in a relevant field (not explicitly mentioned but implied)
- Full-time position with competitive pay and benefits
- Eligibility for benefits including medical, dental, vision, and 401(k) with employer match
- Expected to work in a dynamic and fast-paced environment
- Must be eligible to work in the United States (not explicitly mentioned but implied)
