Autonomous Driving RL Engineer — Lead & Deploy

NVIDIA Corporation; Santa Clara (CA); On-site; USD 224,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a Reinforcement Learning Engineer in Santa Clara, CA to build and deploy cutting-edge RL for autonomous driving. You will design new RL algorithms, develop scalable training environments, and collaborate with perception and planning teams to integrate RL models into the autonomous driving stack.

The role requires a strong RL background, proficiency in PyTorch or TensorFlow, and experience with C++/Python for real-time systems.

Qualifications

• BS or higher in Computer Science, Robotics, Electrical Engineering, or related field.
• 12+ years of experience in the related field.
• Strong 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 algorithms.
• Experience in C++ and Python development for real-time systems.

Responsibilities

• Build and implement brand 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.

Skills

Reinforcement Learning

Policy Gradient RL

Actor-Critic

PyTorch

TensorFlow

C++

Python

Real-time systems

Education

BS in Computer Science / Robotics / Electrical Engineering or related

MS/PhD (advantage)

Job description

NVIDIA is seeking a Reinforcement Learning Engineer in Santa Clara, CA to build and deploy cutting-edge RL for autonomous driving. You will design new RL algorithms, develop scalable training environments, and collaborate with perception and planning teams to integrate RL models into the autonomous driving stack.

The role requires a strong RL background, proficiency in PyTorch or TensorFlow, and experience with C++/Python for real-time systems.