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

Overview

• At NVIDIA, we are seeking exceptional engineers to join our autonomous driving team to design, implement, and deploy cutting-edge end-to-end autonomous driving systems, running on NVIDIA chips in mass-production vehicles.
• Our strategy has evolved from AI 1.0 — building a driver from scratch — to AI 2.0 — teaching an intelligent agent to drive.
• This next phase leverages LLMs, VLMs, and VLAs to bring unprecedented reasoning, planning capabilities, and interactivity with the driving system to autonomous vehicles and general robotics.
• Let’s build the future of autonomy—together! What You’ll Be Doing: Design and train innovative large-scale models—including generative, imitation, and reinforcement learning—to improve the planning and reasoning capabilities of our driving systems.
• Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications.
• Explore novel data generation and collection strategies to improve diversity and quality of training datasets.
• Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met.
• Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.
• What We Need to See: Hands-on experience building LLMs, VLMs, or VLAs from scratch or a proven track record as a top-tier coder passionate about autonomous systems.
• Deep understanding of modern deep learning architectures and optimization techniques.
• Proven record of deploying production-grade ML models for self-driving, robotics, or related fields at scale.
• Strong programming skills in Python and proficiency with major deep learning frameworks.
• Familiarity with C++ for model deployment and integration in safety-critical systems.
• PhD with 2+ years, MS (or equivalent experience) with 4+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
• Ways to Stand Out from the Crowd: Experience with LLM/VLM/VLA systems deployable to autonomous vehicles or general robotics.
• Publications, open-source contributions, or competition wins related to LLM/VLM/VLA systems.
• Deep understanding of behavior and motion planning in real-world AV applications.
• Experience building and training large-scale datasets and models.
• Proven ability to optimize algorithms for real-time performance in resource-constrained environments and strong track record of taking projects from concept to production deployment.