Teams at Zoox

Develop and optimize large-scale ML training systems for autonomous-driving foundation, VLM, and VLA models. Responsibilities include distributed training, mixed precision, reinforcement learning performance, GPU kernel development, profiling, benchmarking, and debugging across compute, compiler, framework, and networking layers. The role collaborates with researchers, data engineers, and hardware engineers to improve training efficiency, scalability, and cost.

Lead ML training performance for large-scale VLM, VLA, foundation-model, and reinforcement-learning systems. Design distributed parallelism, mixed-precision strategies, gradient checkpointing, GPU kernels, profiling workflows, and performance benchmarks. Own RL optimization strategy and roadmap, diagnose bottlenecks across compute and networking layers, partner with research and engineering teams on architecture, and mentor engineers.

Supports autonomous vehicle programs by coordinating cross-functional activities, tracking engineering tasks and parts, managing BOM and PLM data, monitoring milestones and risks, and reporting program status. The role drives action-plan follow-through across engineering, software, autonomy, supply quality, and program stakeholders to meet build, testing, and delivery targets.