Senior RL Engineer for Production-Ready Autonomous Driving

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

Job summary

NVIDIA is seeking a Reinforcement Learning Engineer to advance autonomous driving technology. You will build new RL algorithms for decision-making and planning, and develop scalable training pipelines and sim environments to support RL training.

You will collaborate with perception and planning teams to integrate RL models into the autonomous driving stack and benchmark against baselines in urban scenarios. Production deployment on automotive hardware is expected.

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.
• Strong analytical and problem-solving skills with a track record of implementing and debugging complex RL systems.

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.

Skills

Reinforcement Learning

PyTorch

TensorFlow

C++

Python

Education

BS or higher in Computer Science, Robotics, Electrical Engineering

Tools

PyTorch

TensorFlow

Job description

NVIDIA is seeking a Reinforcement Learning Engineer to advance autonomous driving technology. You will build new RL algorithms for decision-making and planning, and develop scalable training pipelines and sim environments to support RL training.

You will collaborate with perception and planning teams to integrate RL models into the autonomous driving stack and benchmark against baselines in urban scenarios. Production deployment on automotive hardware is expected.