Senior ML Storage Infrastructure Engineer

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.

Zoox is seeking a Senior ML Storage Infrastructure Engineer to work on our custom High-Performance Computing infrastructure and its supporting ecosystem of tools and services. This infrastructure is central to machine learning workflows across all Zoox software divisions, from data engineering to computer vision perception to simulation and more. You will take on a breadth of end-to-end responsibilities including distributed system design, algorithmic job scheduling, and adaptive cloud scaling in support of all of Zoox’s computational needs.

In This Role, You Will

• Design, build, and optimize a petabyte-scale, in-house HPC storage infrastructure, ensuring high performance and reliability for our machine learning workloads across both cloud and on-premise data centers.
• Drive GPU efficiency by strategically collocating storage and compute, architecting a storage layer that keeps tens of thousands of GPUs fully utilized and prevents bottlenecks.
• Drive key initiatives in training and storage optimization by partnering with ML practitioners, applying your deep understanding of frameworks such as PyTorch and TensorFlow to meet their evolving demands.
• Investigate and adopt new distributed system paradigms and cutting-edge technologies to ensure our infrastructure can scale to meet ever-growing computational and storage demands.
• Create production-grade web service APIs, SDKs, and other essential tools to deliver a world-class developer experience for all software teams at Zoox.

Qualifications

• Experience designing and building high-performance, distributed storage systems (object/file) for large-scale, GPU-bound workloads.
• Proficiency in Python, Java, or similar languages for developing data-intensive, high-performance applications.
• Hands-on experience with cloud platforms (AWS, GCP, Azure), using their storage, GPU, and observability services to provide usage showback for ML practitioners.
• Bachelor's degree in Computer Science or a related field with a strong foundation in data structures and systems design.

Bonus Qualifications

• Experience with parallel filesystems (e.g., Lustre, FSx) and their integration with container orchestrators via Kubernetes CSI drivers.
• Deep knowledge of ML frameworks like PyTorch and TensorFlow, and workload schedulers such as SLURM or Kubernetes.
• Familiarity with emerging AI paradigms, including agentic systems, and observability tools like OpenTelemetry.

Compensation

The base salary range for this position is $192,000 - $300,000 annually. Compensation includes salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights, with a potential sign-on bonus. The listed range applies only to the base salary and varies by geographic location and level, determined by factors such as experience, domain knowledge, and interview performance.

Benefits

Zoox offers a comprehensive benefits package, including paid time off (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, and life insurance.

A Final Note

You do not need to match every listed expectation to apply for this position. Zoox values diverse perspectives for innovation and is committed to a team with varied backgrounds, experiences, and skills. AI tools may be used in the hiring process but do not replace human judgment. Final hiring decisions are made by humans.

Key skills/competency

• Distributed Storage Systems
• High-Performance Computing (HPC)
• Machine Learning Infrastructure
• GPU Optimization
• Cloud Platforms (AWS, GCP, Azure)
• Python/Java Development
• System Design
• Data Structures
• ML Frameworks (PyTorch, TensorFlow)
• Kubernetes