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As a Staff Machine Learning Engineer within the Autonomy team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalization, comfort, and collaboration. You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production.
Job Responsibility:
Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization
Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment
Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness
Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development
Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems
Collaborate cross-functionally across various teams to ensure integration and iteration velocity
Mentor senior engineers and shape the long-term technical direction across Autonomy
Requirements:
7+ years (Staff) or 10+ years (Principal) years in ML engineering, with a strong track record of shipping deep learning systems to production
Expert in deep learning (esp. sequential models, control, planning, or perception)
Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices
Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components
Ability to lead technical initiatives across teams, drive alignment, and mentor engineers
Nice to have:
Prior work in autonomous driving, imitation learning, or trajectory prediction
Familiarity with personalization, human behavior modeling, or driver intent inference
Experience integrating ML systems into production hardware or multi-agent simulation
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