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Zipline is the world's largest autonomous drone delivery service, operating on four continents and completing millions of deliveries of blood, vaccines, medical supplies, food, and retail products. The company has safely flown over 140 million commercial autonomous miles and makes a delivery somewhere in the world every 30 seconds.
As an ML Training & Inference Infrastructure Engineer on the Data Platform team, you will build and scale systems powering Zipline's data flywheel. You'll work at the intersection of autonomy and infrastructure, owning systems that make ML development faster, reproducible, observable, and safe for real autonomous systems.
Key responsibilities include:
- Building and operating software infrastructure that enables learning algorithms to leverage Zipline's large-scale fleet data
- Designing scalable, maintainable data and ML infrastructure for autonomy teams, including dataset creation, validation, training, evaluation, and deployment
- Owning and improving data pipelines that feed into the ML development loop
- Identifying and mitigating bottlenecks in the ML development cycle around orchestration, performance, and reproducibility
You should have 3+ years of professional software engineering experience, ideally in ML infrastructure, data infrastructure, robotics, autonomy, aerospace, medical devices, or safety-critical hardware environments. Strong production Python skills, experience building reproducible data and ML pipelines, and familiarity with PyTorch, Kubernetes, AWS, and infrastructure-as-code tools are essential. Bonus experience includes deploying ML systems on real robots/drones, large-scale training systems, feature stores, and annotation/dataset tooling.
