VLA Deep Learning Engineer, End-To-End Autonomous Driving

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 in need of skilled engineers to join our autonomous driving team to invent, execute, and deploy pioneering end-to-end autonomous driving systems. Our strategy has progressed from AI 1.0 — constructing a driver from the ground up — to AI 2.0 — training an intelligent agent to drive. This is achieved by developing LLMs, VLMs, and VLAs to offer exceptional reasoning, planning capabilities, and interaction with the driving system for autonomous driving and general robotics. Let’s innovate the future of autonomy—together!

What You Will Be Doing

• Build and train innovative large-scale models—including generative, imitation, and reinforcement learning—to improve the planning and reasoning capabilities of our driving systems.
• Explore novel data generation and collection strategies to improve diversity and quality of training datasets. Develop, pre-train, and optimize LLM/VLM/VLA models for autonomous driving and robotics applications.
• Collaborate cross-functionally to deploy and integrate AI models into vehicle firmware.
• Deliver production-quality, safety-critical software that meets performance, safety, and reliability standards.
  
  

• PhD or Master's degree with equivalent experience.
• 8+ years of experience
• Hands-on experience training LLMs/VLMs/VLAs from scratch, or a proven record as a top-tier ML engineer/researcher passionate about autonomous systems.
• Strong programming skills in Python and proficiency with major deep learning frameworks. Basic familiarity with C++ for model deployment and integration in safety-critical systems.
• Comprehensive grasp of current deep learning structures and improvement methods. Consistent track record of deploying production-grade ML models for self-driving, robotics, or related fields at scale.
  
  

• Experience developing and shipping LLM/VLM/VLA solutions for autonomous vehicles or general robotics products.
• Publications, contributions to open-source projects, or victories in competitions connected to LLM/VLM/VLA systems.
• Profound comprehension of behavior and motion planning in real-world autonomous vehicle (AV) applications.
• Experience building and training large-scale datasets and models and/or training agents with reinforcement learning.