About the job
As the Director of Engineering - Autonomy, you will provide the strategic leadership and executive vision for the teams responsible for major components of the core autonomy system and its safety architectures. You will build and scale a world-class organization dedicated to advancing Physical AI, focusing on edge-deployed machine learning, deterministic safety fallbacks, and real-time execution. Rather than building a traditional closed ecosystem, your teams will develop the robust onboard intelligence and safety guards required to safely evaluate next-generation models, benchmark autonomous capabilities, and collect high-value, long-tail data in complex real-world environments. Operating at the intersection of advanced research and rigorous systems engineering, you will hold ultimate accountability for ensuring that these core system components maneuver safely, predictably, and intelligently on physical hardware.
Responsibilities
Executive Leadership & Strategy: Define the technical vision and organizational roadmap for major components of the core autonomy system. You will align complex technical goals with Uber’s overarching mission to accelerate the broader autonomous ecosystem
Core AI & Safety Architecture: Direct the development of robust edge software, guiding the integration of advanced ML models with deterministic, rule-based safety fallbacks. Ensure the system exhibits strictly compliant, collision-free operations while adhering to rigorous real-time compute and kinematic constraints on the vehicle
Research & Evaluation Focus: Champion the transition of cutting-edge AI into production-ready software designed specifically to safely orchestrate vehicle operations for advanced research, ecosystem benchmarking, and scaled data collection mission
Organizational Growth: Build, manage, and mentor a high-performing organization of engineering managers, principal engineers, and technical leaders, fostering a culture of rigorous execution and safety-first engineering
Cross-Functional Execution: Partner directly with other AV departments to ensure a seamless transition from virtual testing to physical fleet deployment. You are the definitive owner of your core system components' real-world safety metrics and deployment readiness
Qualifications
Minimum
12+ years of professional experience in Software Engineering, Machine Learning, or Autonomous Systems
5+ years of leadership experience managing and scaling engineering organizations, including experience managing other managers and senior technical leaders (Principals/Staff)
Proven track record of successfully deploying complex, real-time software systems to physical hardware (edge compute, robotics, or autonomous vehicles)
Bachelor's degree in Computer Science, Computer Engineering, or a related technical field
Preferred
Advanced degree (MS/PhD) in Computer Science, Robotics, or a related technical field.
Deep understanding of high-performance system optimization for edge devices (C++, CUDA, Linux, RTOS)
Extensive experience architecting autonomous safety systems, fallback logic, and real-time control constraints
Familiarity with modern AI/ML frameworks (e.g., PyTorch) and the challenges of running deep learning models on constrained in-vehicle compute
Strong background working with ecosystem partners, third-party technology evaluation, or reference architectures