Senior Prediction and Planning Engineer, VLM - Autonomous Vehicles

NVIDIA•Us, CA

10d

About The Position

Join our team at NVIDIA to develop brand new, end-to-end autonomous driving systems for 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 outstanding reasoning, planning capabilities, and interactivity with the driving system to autonomous vehicles and general robotics. Let’s build the future of autonomy—together.

Requirements

Hands-on experience building LLMs, VLMs, or VLAs from scratch or a proven track record as a top-tier coder passionate about autonomous systems.
BS/MS in Computer Science or equivalent experience.
8+ years of experience in the field or meaningful experience
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.

Nice To Haves

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.
Proven ability to optimize algorithms for real-time performance in resource-constrained environments.
Strong track record of taking projects from concept to production deployment.

Responsibilities

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.