Director, Engineering - Autonomous Vehicles - CA, Santa Clara, United States

Originally posted 28 August 2026 by the employer.

This role focuses on an AI-native autonomous driving architecture that integrates traditional safety-focused autonomous systems, fully learned driving approaches, foundation models, and world models.

About the role

This Director-level role involves defining the architecture for NVIDIA’s advanced AI-native autonomous driving system. This architecture integrates traditional safety-focused autonomous systems, fully learned driving approaches, foundation models, and world models into one production platform.

What you'll do

• Set the technical vision and architecture for NVIDIA’s autonomous driving stack, spanning both classical and learning-based approaches.
• Build and lead an organization of engineers and technical leaders across Prediction, Decision Making, Planning, Control, and Safety.
• Define how classical safety-critical autonomy and learned driving systems work together within a unified production architecture.
• Drive the architecture for robust system-level safety, redundancy, fallback, and degraded-mode strategies.
• Lead the development and productionization of end-to-end, data-driven autonomous driving pipelines.
• Advance large-scale vision-language-action (VLA) / driving foundation models and their integration into production autonomous vehicles.

What you'll need

• PhD with 12+ years, MS with 10+ years, or BS (or equivalent experience) with 15+ overall years of relevant industry experience in Computer Science, Computer Engineering, Robotics, Machine Learning, or a related technical field.
• 8+ years of experience leading a team.
• Significant technical leadership experience, including leading senior engineers, architects, and/or engineering managers working on production systems.
• Extensive knowledge of traditional driverless vehicle system designs, including prediction, planning, decision making, control, safety, redundancy, and fallback systems.
• Solid grasp of modern learning-based autonomy, including end-to-end driving models, foundation models, large-scale machine learning systems, or embodied AI.
• Experience driving end-to-end self-driving system builds and understanding interactions involving perception as well as planning and control.
• Demonstrated ability to attract, recruit, mentor, and grow engineering talent.

Skills: autonomous driving architecture, AI-native, foundation models, world models, safety-critical autonomy, learned driving systems