Senior Machine Learning and Simulation Engineer - Autonomous Vehicles

Lead the design and development of large-scale RL training frameworks to accelerate the development of multi-modal AV foundation models.

Design, build, and optimize simulation and data processing pipelines to enable scalable training of driving policies.

Focus on measuring and enhancing simulation quality and refining the reward function for RL training.

Ensure the reliability and performance of training workflows on large GPU clusters through the development of robust monitoring and debugging tools.

Partner with researchers to integrate state-of-the-art model architectures into efficient and scalable training pipelines.

Bachelor's degree in Computer Science, Robotics, Engineering, or a related field (or equivalent experience).

12+ years of relevant professional experience encompassing large-scale ML training, AV systems, simulation, and AI infrastructure development.

Deep proficiency in RL algorithms, such as PPO and GRPO, including practical experience with hyperparameter tuning and reward function design.

Exceptional programming skills in C++ and Python, vital for developing efficient systems and data pipelines.

Extensive experience with large-scale GPU clusters, High-Performance Computing (HPC) environments, and job scheduling/orchestration tools (e.g., Kubernetes, SLURM).

Experience in RL infrastructure or general LLM training/fine-tuning infrastructure in industry.

Experience in simulation & closed-loop evaluation of autonomous driving end-to-end models.

Proven record on large-scale data pipeline development and algorithm optimization.