NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
At NVIDIA, we are pushing the boundaries of what’s possible within self-driving vehicle technology by bringing to bear the power of Deep Reinforcement Learning (RL). As a world leader in AI and high-performance computing, NVIDIA provides an outstanding platform where innovative research meets real-world production. We are looking for a Reinforcement Learning Engineer to join our mission in building intelligent, safe, and efficient self-driving technology that will redefine transportation on a global scale.
What you'll be doing:
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.
What we need to see:
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.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor’s degree or higher in Computer Science, Robotics, Electrical Engineering, or a related field, or equivalent experience
- 12 or more years of experience in a related field
- Strong background in reinforcement learning, including policy gradient methods such as PPO and GRPO, actor-critic architectures, and on-policy and off-policy reinforcement learning
- Proficiency with PyTorch or TensorFlow and practical experience implementing reinforcement learning algorithms
- Experience developing in C++ and Python for real-time systems
- Strong analytical and problem-solving skills, with experience implementing and debugging complex reinforcement learning systems
- Experience shipping autonomous driving features or embodied AI
- Experience with Flow Matching, Diffusion, or autoregressive generative models for policy representation or trajectory modeling
- Experience training policies on rollout distributions and addressing compounding errors in autonomous driving
- Experience with large-scale data flywheels, fleet telemetry scenario mining, auto-labeling pipelines, and automated performance tracking
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
