Design and implement core services and abstractions for distributed compute infrastructure supporting thousands of concurrent jobs

Work with customer teams and other infrastructure teams to build a multiyear software engineering roadmap for the HPC platform

Lead multi-quarter, cross team initiatives that drive org-wide improvements

Create production-grade APIs, SDKs, and tools that make it easy for engineers across Zoox to run large-scale distributed workloads

Design and improve job scheduling algorithms and auto-scaling policies to maximize reliability and resource availability

Design multi-region orchestration strategies that optimize for data locality, reliability, and performance

Identify and resolve systemic reliability and performance issues through profiling, analysis, and collaboration with workload owners across multiple teams

Evaluate new technologies and paradigms that improve Zoox's computational and storage capabilities

Develop capacity planning tools and forecasting models to support Zoox's growing compute needs

Mentor junior engineers, guiding them through their career development

Experience designing and operating large-scale distributed systems in production

Experience with Ray.io, particularly Ray Core and Ray Data (or equivalent technologies)

Experience with Kubernetes, particularly for heterogeneous workloads

Experience with cloud infrastructure on AWS or similar providers

Track record of shipping and operating reliable, scalable infrastructure

Demonstrated ability to prioritize development work and build cross-functional consensus around technical tradeoffs

Proficiency with Python

Exposure to machine learning workloads (training, inference, data generation)

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

Experience building developer tools and platforms used by large engineering organizations