Staff Software Engineer - GPU System Software, Onboard Execution (AI)

Location: Hybrid in Foster City, CA, Boston, MA, or Seattle, WA, United States

Base salary: USD 290,000–322,000 per year; additional compensation may include Amazon RSUs, Zoox Stock Appreciation Rights, and a possible sign-on bonus.

Company

Zoox is developing a purpose-built, fully autonomous robotaxi fleet and the supporting mobility ecosystem, combining robotics, machine learning, and design for urban transportation.

What you will do

• Define the technical vision for GPU execution across Zoox’s onboard AI workload.
• Develop the strategy for scheduling, sharing, isolating, and sizing GPU workloads on safety-critical, real-time vehicle computers.
• Drive improvements that enable more AI compute while reducing power, thermal load, cost, and hardware requirements.
• Own onboard compute strategy across future vehicle generations.
• Collaborate with Perception, Prediction, Planning, ML Infrastructure, Middleware, and Hardware organizations.
• Act as a senior technical voice for onboard compute across the company.

Requirements

• Technical leadership of GPU system software and onboard execution strategy for AI workloads.
• Experience addressing end-to-end compute performance across GPU execution, profiling, observability, and compute platforms.
• Ability to work across machine learning, autonomy, middleware, and hardware organizations.
• Experience with safety-critical, real-time, fixed-power, and fixed-thermal computing constraints.
• Relevant years of experience, domain knowledge, and technical depth appropriate to the level being considered.

Culture & Benefits

• Work in a fast-moving, highly execution-oriented environment focused on autonomous mobility.
• Paid time off, including sick leave, vacation, and bereavement leave, plus unpaid time off.
• Health, long-term care, short-term and long-term disability, and life insurance.
• Amazon Restricted Stock Units and Zoox Stock Appreciation Rights.
• Compensation and leveling vary based on geographic location, experience, domain knowledge, and interview performance.