Senior Machine Learning and Simulation Engineer - Autonomous Vehicles

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior ML Engineer focused on building and optimizing large-scale Reinforcement Learning (RL) training frameworks for multi-modal Autonomous Vehicle (AV) foundation models. This role involves designing simulation and data processing pipelines, refining reward functions, and ensuring the reliability of training workflows on GPU clusters, with a focus on closed-loop simulation for training end-to-end AV models.

What you'd actually do

  1. Lead the design and development of large-scale RL training frameworks to accelerate the development of multi-modal AV foundation models.
  2. Design, build, and optimize simulation and data processing pipelines to enable scalable training of driving policies.
  3. Focus on measuring and enhancing simulation quality and refining the reward function for RL training.
  4. Ensure the reliability and performance of training workflows on large GPU clusters through the development of robust monitoring and debugging tools.
  5. Partner with researchers to integrate state-of-the-art model architectures into efficient and scalable training pipelines.

Skills

Required

  • C++
  • Python
  • Reinforcement Learning (RL) algorithms
  • hyperparameter tuning
  • reward function design
  • large-scale GPU clusters
  • High-Performance Computing (HPC)
  • job scheduling/orchestration tools (e.g., Kubernetes, SLURM)

Nice to have

  • RL infrastructure
  • general LLM training/fine-tuning infrastructure
  • simulation & closed-loop evaluation of autonomous driving end-to-end models
  • large-scale data pipeline development
  • algorithm optimization

What the JD emphasized

  • Deep proficiency in RL algorithms, such as PPO and GRPO, including practical experience with hyperparameter tuning and reward function design.
  • Extensive experience with large-scale GPU clusters, High-Performance Computing (HPC) environments, and job scheduling/orchestration tools (e.g., Kubernetes, SLURM).

Other signals

  • productizing ML solutions
  • large-scale RL training frameworks
  • multi-modal AV foundation models
  • scalable training of driving policies
  • integrating state-of-the-art model architectures