Senior Machine Learning and Simulation Engineer - Autonomous Vehicles

Nvidia•Santa Clara, CA

4d•$224,000 - $425,500

About The Position

We are seeking exceptional Senior Machine Learning and Simulation Engineers to join NVIDIA's Autonomous Vehicles (AV) Simulation team! This role requires strong technical leadership and outstanding software engineering skills, coupled with deep expertise in both simulation and artificial intelligence, including deep learning, reinforcement learning, end-to-end driving and Physics AI models. The successful candidate will have a solid track record of productizing ML solutions for autonomous driving and simulation at scale. This position centers on developing a Closed-Loop Simulation-based Reinforcement Learning (RL) framework in order to train advanced end-to-end AV models, such as Alpamayo R1. This position will design and improve the accuracy and performance of the RL framework and simulation, leveraging SOTA techs including NuRec, Traffic Models, and Cosmos World Model. Success in this role requires close collaboration with the AV Platform, AV Product, and Research teams.

Requirements

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).

Nice To Haves

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.

Responsibilities

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.