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Lead safety strategy and deployment readiness for Level 4 autonomous driving systems. Define safety requirements, system architectures, release policies, fallback criteria, V&V plans, evaluation metrics, emergency protocols, and operational safety processes. Analyze incidents, improve test coverage and procedures, and collaborate with software, product, operations, and safety teams to support unmanned deployment and commercial scaling.

Develop core decision-making and motion planning systems for Level 4 autonomous vehicles. Responsibilities include behavioral planning, path and velocity generation, trajectory optimization, world modeling, trajectory cost design, testing, debugging, and root-cause analysis. The engineer will collaborate with Prediction, Perception, and Control teams to deliver safe, comfortable, and intelligent vehicle motion in complex environments.

Lead company-wide AI transformation by designing unified distributed AI platforms, integrating LLM ecosystems, and embedding with autonomous driving teams. Build scalable LLMOps and MLOps pipelines, optimize distributed training, inference, GPU utilization, and production latency, and drive adoption of AI-native engineering practices. Provide technical vision, roadmap leadership, and hands-on systems engineering across perception, prediction, planning, control, and simulation teams.

Coordinate autonomous driving software programs from planning through release. Break strategies into milestones, deliverables, owners, and timelines; track progress, manage release logistics, document workflows, facilitate cross-functional meetings, identify risks, report status, and drive corrective actions. The role partners with engineering and operations teams to improve development processes and support production readiness.

Lead deployment, inference optimization, resource scheduling, profiling, and stability initiatives for AI models across autonomous vehicles and cloud infrastructure. Design low-latency inference pipelines, optimize GPU execution and memory usage, support LLM serving and model validation, diagnose software-hardware integration issues, and collaborate with perception, prediction, cloud infrastructure, and safety teams.

Lead the architecture and implementation of infrastructure for autonomous driving software, including low-latency IPC middleware, runtime engines, compiler and build toolchains, simulation platforms, crash triage, and diagnostic systems. Drive performance optimization across embedded platforms, establish engineering standards, mentor engineers, and guide cross-functional architecture and technical initiatives.

Develop motion and behavioral planning algorithms for Level 4 autonomous vehicles. Responsibilities include lane-change and merge decision-making, trajectory optimization, geometry and velocity planning, world modeling, cost-function design, simulation and real-world testing, debugging, and collaboration with perception, prediction, and control teams. The role requires PhD-level research experience in robotics or related fields, strong C++ skills, and publications in leading conferences.

Develop core decision-making and motion-planning systems for Level 4 autonomous vehicles. Responsibilities include behavioral planning, lane changes, merges, path and velocity generation, world modeling, trajectory cost design, optimization, testing, debugging, and root-cause analysis. Collaborate with Prediction, Perception, and Control teams to enable safe, comfortable vehicle motion.

Design and implement map fusion, online BEV vectorization, and motion planning systems for autonomous vehicles. Build scalable C++/Python infrastructure, deploy deep-learning mapping models (ONNX→C++/TensorRT), optimize real-time planner latency and safety, and create anomaly detection for map/scene changes.