onsite
Machine Learning Engineer - Simulation Framework - Zoox
ML Engineer
Develop and optimize machine‑learning models for a high‑performance, GPU‑based simulation framework, enabling large‑scale synthetic driving scenarios and improving sim‑to‑real transfer for autonomous vehicle validation.
About the role
Key Responsibilities
- Design, implement, and optimize ML pipelines that run efficiently on GPU‑accelerated simulation environments.
- Develop algorithms for sim‑to‑sim and sim‑to‑real domain adaptation, improving fidelity of synthetic scenarios.
- Collaborate with simulation engineers to integrate ML components into the core simulation framework and ensure scalability.
- Profile and benchmark performance, applying CUDA and low‑level optimizations to meet real‑time requirements.
- Research and apply state‑of‑the‑art techniques in perception, prediction, and control within synthetic worlds.
Requirements
- Strong programming experience in Python and C++, with hands‑on CUDA development.
- Deep knowledge of machine‑learning frameworks such as PyTorch or TensorFlow, and experience training large models.
- Proven ability to work on high‑performance, GPU‑centric systems and optimize code for speed and memory usage.
- Background in simulation, robotics, or autonomous driving, including familiarity with synthetic environment generation.
- Excellent problem‑solving skills and ability to work cross‑functionally with software and hardware teams.
Skills
pythonccudapytorchmachine learning