Reinforcement Learning Intern - 2027

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 Intern 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: Research, prototype, and implement state-of-the-art Reinforcement Learning (RL) algorithms for autonomous vehicle decision-making and trajectory planning. Assist in developing and scaling training pipelines and simulation environments for complex RL experiments. Collaborate closely with senior researchers and engineers across perception and planning teams to integrate RL models into the autonomous driving stack. Analyze and benchmark RL model performance against imitation learning and traditional baselines in complex urban simulation scenarios. Explore techniques to optimize and lightweight RL policies for real-time deployment on automotive hardware. What we need to see: Currently pursuing an MS or PhD in Computer Science, Robotics, Electrical Engineering, or a related quantitative field with a strong academic background. Solid theoretical foundation in Reinforcement Learning, including policy gradients (e.g., PPO, GRPO), actor-critic methods, and model-based RL. Hands-on project or academic research experience developing and training deep RL algorithms using PyTorch or TensorFlow. Strong programming skills in Python and foundational familiarity with C++. Great eagerness to learn, strong problem-solving abilities, and excellent communication skills for collaborative team research. Ways to stand out from the crowd: Publication record at top-tier AI/Robotics conferences (e.g., NeurIPS, ICML, IROS, ICRA, CoRL) or relevant open-source contributions in RL or autonomous driving. Research exposure to generative modeling (e.g., Flow Matching, Diffusion, AR decoders) applied to trajectory prediction or world models. Experience addressing compounding errors, distribution shift, or safety constraints in robotic control and autonomous navigation simulation environments.