Senior RL Engineer, Autonomous Driving - Equity + Impact

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

Job summary

NVIDIA in Santa Clara, California is seeking a Reinforcement Learning Engineer to advance autonomous driving by designing new RL algorithms for decision-making and planning, and by building scalable training pipelines and simulation environments.

The role requires a BS or higher in CS/Robotics/EE and 12+ years of related experience, deep RL knowledge (policy gradients such as PPO/GRPO), and proficiency in PyTorch or TensorFlow with C++/Python for real-time systems.

Qualifications

• BS or higher in Computer Science, Robotics, Electrical Engineering, or related field.
• 12+ years in the related field.
• Strong RL background, including policy gradient methods (PPO, GRPO).
• Proficiency in PyTorch or TensorFlow and RL algorithms.
• Experience in C++ and Python development for real-time systems.
• Strong analytical and problem-solving skills with complex RL systems.

Responsibilities

• Build and implement brand new RL algorithms for autonomous vehicle decision-making and planning.
• Develop scalable training pipelines and simulators for RL.
• Collaborate with perception and planning teams to integrate RL models into the stack.
• Benchmark RL models against imitation learning baselines in urban environments.
• Optimize and deploy RL models to production-grade automotive hardware.

Skills

Reinforcement Learning

Policy gradient methods (PPO, GRPO)

PyTorch

TensorFlow

C++

Python

Real-time systems

Education

BS or higher in Computer Science, Robotics, or Electrical Engineering

Tools

PyTorch

TensorFlow

C++

Python

Job description

NVIDIA in Santa Clara, California is seeking a Reinforcement Learning Engineer to advance autonomous driving by designing new RL algorithms for decision-making and planning, and by building scalable training pipelines and simulation environments.

The role requires a BS or higher in CS/Robotics/EE and 12+ years of related experience, deep RL knowledge (policy gradients such as PPO/GRPO), and proficiency in PyTorch or TensorFlow with C++/Python for real-time systems.