ML Data Infrastructure Engineer

Details

As a member of the ML Data Platform team, you will solve technical challenges, including upgrading and implementing state-of-the-art software infrastructure. The team builds a high-performance, high-availability, globally distributed ecosystem platform of services that provides the foundation for rapid development of novel systems.

Responsibilities

• Design and build data processing infrastructure for model training and feature serving, optimizing for performance, reproducibility, and traceability.
• Collaborate closely with research teams to design and implement novel data processing architectures for emerging model and training paradigms.
• Identify and resolve performance bottlenecks across the training data pipeline, from raw data ingestion to feature delivery.
• Establish best practices and tooling for data infrastructure used across ML teams.

Requirements

• 1–3 years of experience and a minimum of a BS and/or MS in Computer Science.
• Strong software engineering fundamentals, with experience building high-throughput, fault-tolerant distributed systems.
• Hands-on experience with distributed computing frameworks such as Apache Spark or Flink.
• Solid grounding in data structures, systems design, and performance optimization.
• Strong problem-solving skills and attention to detail.

Preferred Qualifications

• Background in MLOps, data infrastructure, or ML infrastructure.
• Experience with ML training pipelines, feature stores, or model-serving systems.

Compensation and Benefits

• CA base pay range: $150,000–$224,000 USD per year.
• Equity eligible.
• Medical, dental, vision, life, and disability insurance.
• 401(k) retirement plan.
• Unlimited discretionary time off.
• 10 paid holidays per year.
• 80 hours of paid sick leave.

Application

• Apply online.
• The application window is expected to close within 30 days of the posting date.