Senior Deep Reinforcement Learning Engineer - Autonomous Driving

NVIDIA•Santa Clara, CA

9d•$224,000 - $356,500

About The Position

NVIDIA is seeking a Reinforcement Learning Engineer to develop intelligent, safe, and efficient self-driving technology. The role applies deep reinforcement learning to autonomous vehicle decision-making and planning, combining innovative research with production deployment. The engineer will build RL algorithms, develop scalable training pipelines and simulation environments, integrate models into the autonomous driving stack, benchmark against imitation-learning baselines, and optimize models for automotive hardware.

Requirements

Bachelor's degree or higher in Computer Science, Robotics, Electrical Engineering, or a related field, or equivalent experience.
12 or more years of experience in a related field.
Solid reinforcement-learning background, including policy-gradient methods such as PPO and GRPO, actor-critic architectures, and on-policy and off-policy RL.
Proficiency in PyTorch or TensorFlow and practical experience with RL-related algorithms.
Experience developing in C++ and Python 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 autoregressive-based decoders) for policy representation or trajectory modeling.
Experience training policies on their own rollout distributions and handling compounding-error problems in autonomous driving.
Experience with large-scale data flywheels, including mining scenarios from fleet telemetry logs, auto-labeling pipelines, and automated performance tracking.

Responsibilities

Build and implement new reinforcement learning 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.