Senior Deep Reinforcement Learning Engineer, Autonomous Driving

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $224,000–$356,500 / yr
Posted: 15 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $221k

This role $290k

$163k most similar roles pay here $377k

This role pays more than 92% of similar roles. Most pay $187,137–$254,750 — the shaded band above. At the midpoint, this role pays about $290k versus about $221k for comparable roles.

Based on 240 similar postings.

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About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior Deep Reinforcement Learning Engineer, Autonomous Driving

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As a Senior Deep Reinforcement Learning Engineer - Autonomous Driving, you will join the team to build intelligent, safe, and efficient self-driving technology. You will develop and implement brand new reinforcement learning algorithms for autonomous vehicle decision-making and planning while maintaining scalable training pipelines and simulation environments. Your daily work involves collaborating with perception and planning teams to integrate models into a unified autonomous driving stack, benchmarking performance against imitation learning baselines in complex urban environments, and deploying models to production-grade automotive hardware. You will utilize PyTorch or TensorFlow, along with C++ and Python for real-time systems. The role focuses on solving the technical challenges of autonomous vehicle navigation through policy gradient methods, actor-critic architectures, and potentially incorporating generative models like Flow Matching or Diffusion for trajectory modeling within the self-driving domain.

Skills

Reinforcement Learning PyTorch TensorFlow Python C++ Deep Reinforcement Learning PPO GRPO Actor-Critic Imitation Learning Generative Models Flow Matching Diffusion Autonomous Driving Simulation Environments Data Flywheels High-Performance Computing

What you'll do

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.
Integrate RL models into the unified autonomous driving stack with perception and planning teams.
Benchmark RL model performance against imitation learning baselines in complex urban environments.
Optimize and deploy RL models onto production-grade automotive hardware.
Implement and debug complex RL systems using PyTorch or TensorFlow.
Develop real-time systems using C++ and Python.

What we're looking for

BS or higher in Computer Science, Robotics, Electrical Engineering, or a related field (or equivalent experience).
12+ years of experience in the related field.
Solid background in Reinforcement Learning, including policy gradient methods, actor-critic architectures, and on/off-policy RL.
Proficiency in PyTorch or TensorFlow with real experience in RL-related algorithms.
Experience in C++ and Python development for real-time systems.
Strong analytical and problem-solving skills to implement and debug complex RL systems.
Preferred experience in shipping autonomous driving features or embodied AI.
Experience with generative models, large-scale data flywheels, and training policies on rollout distributions.