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# Senior Deep Reinforcement Learning Engineer - Autonomous Driving

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $224,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, C++ (Programming Language), Software Debugging, Python (Programming Language), Tensorflow, Reinforcement Learning, Real Time Systems, Pytorch, Generative AI, Information Technology, Stable Diffusion
- **Published:** August 28, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Deep-Reinforcement-Learning-Engineer---Autonomous-Driving_JR2022179

## About the Role

* BS or higher in Computer Science, Robotics, Electrical Engineering, or a related field (or equivalent experience).
 * 12+ years of experieence in the related field.
 * Solid 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 algorithm
 * 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.

Ways to stand out from the crowd:

 * Background in shipping autonomous driving features or embodied AI.
 * Experience with generative models (Flow Matching, Diffusion, or AR-based decoders) in the context of policy representation or trajectory modeling.
 * Experience with training policies on their own rollout distributions and handling the compounding error problems inherent in autonomous driving.
 * Experience working with large-scale data flywheels, including mining scenarios from fleet telemetry logs, auto-labeling pipelines, and automated performance tracking.

## Description

* 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.


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