# Senior Deep Reinforcement Learning Engineer - Autonomous Driving

**Company**: NVIDIA
**Location**: Santa Clara, CA
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Deep-Reinforcement-Learning-Engineer---Autonomous-Driving_JR2022179?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_91a2c517-b4b

## Description

NVIDIA is seeking a Senior Deep Reinforcement Learning Engineer to join their autonomous driving team. The successful candidate will work on building and implementing new Reinforcement Learning (RL) algorithms for autonomous vehicle decision-making and planning.

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.

Requirements:

- 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 (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.

Nice to Have:

- 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.

Benefits:

- Equity

- Benefits

## Skills

### Required
- Reinforcement Learning
- PyTorch
- TensorFlow
- C++
- Python
- Deep Learning

### Nice to have
- Autonomous Driving
- Generative Models
- Large-scale Data Flywheels

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Source: [Apply at nvidia.wd5.myworkdayjobs.com](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Deep-Reinforcement-Learning-Engineer---Autonomous-Driving_JR2022179?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)