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

Responsibilities

• Design and train innovative large-scale models, including generative, imitation, and reinforcement learning, to enhance planning and reasoning in driving systems
• Build, pre-train, and fine-tune LLM, VLM, and VLA systems for real-world autonomous driving and robotics applications
• Explore novel data generation and collection strategies to improve the diversity and quality of training datasets
• Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability
• Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software

Requirements

• Hands-on experience building LLMs, VLMs, or VLAs from scratch or a proven track record in 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 or MS (or equivalent experience) with 4+ years of relevant experience in Computer Science, Computer Engineering, or a related field

Preferred Qualifications

• Experience with LLM/VLM/VLA systems specifically 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

About the Company

NVIDIA is a global leader in AI computing. Our autonomous driving team is designing the next generation of intelligent agents, moving from AI 1.0 to AI 2.0 by leveraging LLMs, VLMs, and VLAs to bring unprecedented reasoning and interactivity to autonomous vehicles and robotics.