Staff Software Engineer, HPC

Team: Software Systems

Location: Foster City, CA, Boston, MA, Seattle, WA

Commitment: Full-time

Workplace Type: hybrid

Salary:

In this role, you will:

• 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

Qualifications

• 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

Bonus Qualifications

• 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