Senior Software Engineer - HPC Cost Optimization & Efficiency
About The Position
Zoox is looking for an experienced Software Engineer to drive cost optimization and efficiency improvements across our custom High-Performance Computing infrastructure managing annual compute spend. As Zoox scales its autonomous vehicle development, intelligent resource management and cost efficiency have become critical to our success. You will modernize our HPC platformâbuilt on industry-leading technologies like Ray.io, SLURM, and Kubernetesâwith a focus on maximizing utilization, eliminating waste, and reducing cloud costs while maintaining world-class developer velocity. These HPC services form the backbone of development workflows across all Zoox software teams, from data engineering to training our AI models in Perception, Planner, Prediction, to Simulation, and more. You will have a direct impact on Zoox's bottom line through measurable cost reductions and efficiency gains. The position comes with a high degree of independence and the opportunity to define Zoox's compute economics strategy, both technically and organizationally. You will work closely with stakeholders in Autonomy and Software teams to balance performance requirements with cost constraints, incorporating FinOps best practices and the latest cost optimization techniques.
Requirements
- Experience optimizing large-scale distributed systems for cost and efficiency
- Experience with Ray.io, particularly Ray Core and Ray Data
- Experience with Kubernetes, particularly for heterogeneous workloads and cost optimization
- Experience with cloud cost management on AWS (Cost Explorer) or similar providers
- Track record of achieving measurable cost reductions in production infrastructure
- Demonstrated ability to prioritize development work and build cross-functional consensus around cost/performance tradeoffs
- Proficiency with Python
Nice To Haves
- Understanding of FinOps principles and practices
- Experience building cost attribution, chargeback, or showback systems
- Exposure to machine learning workloads (training, inference, data generation) from a cost optimization perspective
- Experience with Kubernetes or SLURM at scale (>10k+ nodes)
- Experience with SLURM workload manager and advanced scheduling policies
- Background in algorithmic optimization or operations research
Responsibilities
- Design and implement cost optimization strategies across distributed compute infrastructure, targeting millions in annual savings
- Build cost visibility, attribution, and chargeback systems to drive accountability and informed decision-making
- Optimize job scheduling algorithms and auto-scaling policies to maximize resource utilization and minimize idle capacity
- Design multi-region orchestration strategies that optimize for data locality, cost and performance
- Identify and eliminate inefficient workload patterns through profiling, analysis, and developer education by coordinating with workload owners across multiple teams
- Evaluate new technologies and paradigms that reduce cost while meeting Zoox's computational and storage needs
- Develop cost forecasting models and budget management tools for capacity planning
- Implement cloud cost optimization strategies including spot instances, reserved capacity, and right-sizing
- Create production-grade APIs, SDKs, and tools that make cost-efficient patterns the default developer experience
Benefits
- paid time off (e.g. sick leave, vacation, bereavement)
- unpaid time off
- Zoox Stock Appreciation Rights
- Amazon RSUs
- health insurance
- long-term care insurance
- long-term and short-term disability insurance
- life insurance
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
Number of Employees
1,001-5,000 employees
