Senior Deep Reinforcement Learning Engineer - Autonomous Driving

NVIDIA•Santa Clara, CA

1d•$224,000 - $356,500

About The Position

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. At NVIDIA, we are pushing the boundaries of what’s possible within self-driving vehicle technology by bringing to bear the power of Deep Reinforcement Learning (RL). As a world leader in AI and high-performance computing, NVIDIA provides an outstanding platform where innovative research meets real-world production. We are looking for a Reinforcement Learning Engineer to join our mission in building intelligent, safe, and efficient self-driving technology that will redefine transportation on a global scale.

Requirements

BS or higher in Computer Science, Robotics, Electrical Engineering, or a related field (or equivalent experience).
12+ years of experieence in the related field.
Solid background in Reinforcement Learning, including policy gradient methods (PPO, GRPO), actor-critic architectures, on-policy and off-policy RL
Proficiency in PyTorch or TensorFlow and real experience with RL-related algorithm
Experience in C++ and Python development for real-time systems.
Strong analytical and problem-solving skills, with a track record of implementing and debugging complex RL systems.

Nice To Haves

Background in shipping autonomous driving features or embodied AI.
Experience with generative models (Flow Matching, Diffusion, or AR-based decoders) in the context of policy representation or trajectory modeling.
Experience with training policies on their own rollout distributions and handling the compounding error problems inherent in autonomous driving.
Experience working with large-scale data flywheels, including mining scenarios from fleet telemetry logs, auto-labeling pipelines, and automated performance tracking.

Responsibilities

Build and implement brand new Reinforcement Learning (RL) algorithms for autonomous vehicle decision-making and planning.
Develop and maintain scalable training pipelines and simulation environments for RL training.
Collaborate with perception, and planning teams to integrate RL models into the unified autonomous driving stack.
Benchmark RL model performance against imitation learning baselines in complex urban environments.
Optimize and deploy RL models to production-grade automotive hardware.