ML Infrastructure Engineer
Zipline · South San Francisco, California, USA
About this role
**ML Infrastructure Engineer** **About Zipline** Zipline is the world's largest and most experienced drone delivery service, operating on four continents and completing millions of deliveries of blood, vaccines, medical supplies, food, and retail products. We design, build, and operate the world's largest autonomous logistics system. **About The Role** As an ML Training & Inference Infrastructure Engineer on the Data Platform team, you'll build and scale systems powering our data flywheel. You'll work at the intersection of autonomy and infrastructure, owning systems that make ML development faster, reproducible, observable, and safe. **What You'll Do** • Build and operate software infrastructure enabling learning algorithms to leverage large-scale fleet data • Design scalable, maintainable data and ML infrastructure for autonomy teams • Own and improve data pipelines feeding into the ML development loop • Identify and mitigate bottlenecks in the ML development cycle **What You'll Bring** • 3+ years professional software engineering experience (ML infrastructure, data infrastructure, robotics, autonomy, or safety-critical environments preferred) • Strong Python production experience; comfort designing APIs, services, and operational workflows • Experience building reproducible data and ML pipelines • Working knowledge of ML concepts and modern deep learning workflows • Experience with PyTorch or similar ML frameworks • Kubernetes or container orchestration experience • AWS and infrastructure-as-code tools (Terraform/CloudFormation) experience • Strong ownership, clear communication, and interest in secure systems **Bonus Points** • ML systems deployment on robots, autonomous vehicles, or drones • Large-scale training systems, feature stores, or model registries • Annotation systems or active-learning workflows **Compensation** $160,000 - $250,000 (based on level and experience) Zipline is an equal opportunity employer. We welcome applications from those traditionally underrepresented in tech.
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