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 platform—built on industry-leading technologies like Ray.io, SLURM, and Kubernetes—with 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.
In this role, you will:
Design and implement cost optimization strategies across distributed compute infrastructure, targeting millions in annual savings
Work with customer teams and other infrastructure teams to build a multiyear software engineering roadmap to optimize compute efficiency across all Zoox workloadsC
Create production-grade APIs, SDKs, and tools that make cost-efficient patterns the default developer experience
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 and demand forecasting models and budget management tools for capacity planning
Qualifications
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
Bonus Qualifications
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
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
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A Final Note: You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
