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Develop distributed computing algorithms, performance metrics, and analysis tools for Zoox’s Collision Avoidance System. Analyze petabytes of urban driving data, evaluate system performance, and collaborate with CAS Perception, Planning, Systems Design, Mission Assurance, and QA engineers to define autonomous-driving validation metrics and plans.

Lead strategic sourcing and supplier partnerships for vehicle direct materials and operations infrastructure. Drive RFQs, negotiations, contract management, supplier performance, cost reduction initiatives, total cost of ownership models, and cross-functional stakeholder alignment to support vehicle development and autonomous ride-hailing operations.

Design and optimize on-vehicle perception inference: allocate CPU/GPU resources, compress and quantize large multimodal models, build TensorRT model conversion pipelines, and deliver production-quality low-latency C++/CUDA inference for power- and thermal-constrained vehicle SoCs.

As a Senior/Staff Software Engineer, you will improve robotic driving behaviors through algorithm development, testing, and performance reviews while ensuring safety and innovation.

Design, build, and operate Zoox's HPC platform supporting thousands of concurrent jobs. Modernize infrastructure (Ray, SLURM, Kubernetes, AWS), improve scheduling, autoscaling, multi-region orchestration, capacity planning, profiling, and developer tooling. Lead cross-team initiatives and mentor junior engineers.

Lead the development of machine learning algorithms focusing on perception for autonomous driving, collaborating across teams and mentoring ML developers.

The Software Engineer will develop algorithms and code related to AI, robotics, and sensing technologies, impacting key systems at Zoox.

Develop advanced Vision-Language-Action models for robotaxis, leading data strategies and improving driving behaviors using machine learning infrastructure.

Design and develop auto-labeling algorithms for autonomous driving, collaborating with ML engineers to enhance data quality and efficiency in model training.

Leads supplier quality and industrialization activities for prototype and production vehicle components. Assesses supplier processes, manufacturability, tooling, and readiness; manages APQP through PPAP and start of production; reviews quality documentation and metrics; leads RCCA, containment, corrective actions, audits, and continuous improvement. Supports material disposition and MRB activities while applying SPC, capability studies, lean manufacturing, and quality systems. Requires up to 20% domestic travel.