• Developing technical strategy and architecture for foundation models as unified world models
• Developing multi-modal, multi-task transformer-based systems that support closed-loop autonomy
• Building training and evaluation pipelines at scale across petabytes of real-world and simulated driving data
• Collaborating with cross-functional teams across perception, planning, simulation, and ML infrastructure
• Driving alignment between model capabilities and real-world deployment constraints (latency, robustness, validation)
• Publishing internal technical guidance and mentoring engineers across autonomy ML

• B.S., M.S., or Ph.D. in Computer Science, Robotics, or a related field
• 7+ years of experience building and deploying large-scale ML systems
• Deep understanding of foundation models, self-supervised learning, and world models in robotics or simulation
• Strong software engineering background, with fluency in Python and C++
• Experience training and evaluating transformer models or end-to-end autonomous agents
• Familiarity with real-time inference systems and autonomous vehicle constraints
• Proven leadership in driving ML projects from research to production

Bonus:

• Prior work on end-to-end autonomous driving architectures (e.g., imitation learning, behavior cloning, world models)
• Experience with sensor fusion (LiDAR, camera, radar) in a learned model

Equal Opportunity

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