Teams at Zoox

Lead the technical vision and strategy for GPU execution across Zoox’s onboard AI platform. Own how perception, prediction, and planning workloads are scheduled, shared, isolated, and optimized across vehicle GPUs under fixed power, thermal, cost, safety, and real-time constraints. Partner with ML, autonomy, middleware, hardware, and infrastructure teams to improve onboard compute efficiency across future vehicle generations.

Lead evaluation strategy for autonomous vehicle perception systems by designing metrics, datasets, statistical methods, and validation frameworks. Analyze large-scale fleet and simulation data to identify failure modes and inform release decisions. Partner cross-functionally with perception, ML, systems, safety, and autonomy teams; mentor data scientists; and present recommendations to senior leadership.

Lead the architecture and strategy for onboard GPU execution in Zoox autonomous vehicles. Define GPU scheduling, multi-tenancy, isolation, deterministic latency, compute budgets, hardware selection, and inference performance direction. Drive end-to-end reductions in latency, utilization, power, and cost across models, runtimes, middleware, and hardware. Provide technical leadership, mentor senior engineers, align cross-functional teams, and lead platform bring-up and performance optimization.