Senior Machine Learning and Simulation Engineer - Autonomous Vehicles - CA, Santa Clara, United States

Originally posted 29 August 2026 by the employer. Relisted by the employer on 30 August 2026.

Develop closed-loop simulation-based RL frameworks using Cosmos World Model, NuRec, and large GPU clusters.

About the role

This role involves developing a closed-loop simulation-based Reinforcement Learning (RL) framework to train autonomous vehicle foundation models. The work incorporates technologies such as NuRec, Traffic Models, and Cosmos World Model to build training pipelines at scale.

What you'll do

- Lead the design and development of large-scale RL training frameworks for multi-modal AV foundation models. - Design, build, and optimize simulation and data processing pipelines to support scalable training of driving policies. - Measure and enhance simulation quality while refining reward functions for RL training. - Ensure the reliability and performance of training workflows on large GPU clusters by developing monitoring and debugging tools. - Partner with researchers to integrate model architectures into scalable training pipelines.

What you'll need

- Bachelor's degree in Computer Science, Robotics, Engineering, or a related field (or equivalent experience). - 12+ years of relevant professional experience across large-scale ML training, AV systems, simulation, and AI infrastructure development. - Proficiency in RL algorithms, such as PPO and GRPO, including hyperparameter tuning and reward function design. - Programming skills in C++ and Python for developing systems and data pipelines. - Experience with large-scale GPU clusters, High-Performance Computing (HPC) environments, and job scheduling tools such as Kubernetes and SLURM.

Skills: Machine Learning, simulation, autonomous driving, Reinforcement Learning, GPU clusters, AI infrastructure