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Lead safety strategy and deployment readiness for unmanned autonomous driving systems. Define release criteria, safety requirements, system architecture, and V&V plans. Develop safety evaluation methodologies, metrics, and incident review processes. Collaborate with software, product, operations, and safety teams to enable safe, scalable commercial unmanned operations.

Develop and implement core motion planning and decision-making systems for autonomous vehicles, including geometry and velocity planning, trajectory optimization, scenario modeling, cost design for trajectory ranking, system analysis and debugging, and cross-team collaboration with Perception, Prediction, and Control.

Design and implement motion and behavioral planning algorithms for autonomous vehicles, integrating geometry and velocity planning, modeling multi-agent interactions, formulating cost/optimization frameworks, and testing systems in simulation and real-world data while collaborating with Perception, Prediction, and Control teams.

Design and implement map fusion and motion planning software for autonomous vehicles. Build data pipelines, integrate HD maps with real-time perception, deploy DETR-style BEV mapping networks, develop trajectory generation and optimization, create anomaly detection for map reliability, and optimize low-latency, multi-threaded C++ runtime for safety-critical planning systems.

Lead design and implementation of a distributed AI/LLM platform, embed with autonomous-driving teams to remove engineering bottlenecks, build LLMOps/MLOps automation for training and inference, optimize GPU cluster utilization and latency, and set technical roadmap for enterprise AI transformation.