opengreenhousea16z
Senior Manager, Data Platform & Autonomy Infrastructure
Zipline
LocationSouth San Francisco, California, USA, Aviary - SSF
Last observed2026-06-23 23:25:44.662340
Job ida16z-zipline:greenhouse:7663467003
About Zipline Zipline is the world’s largest and most experienced drone delivery service. We are on a mission to serve all humans equally by ensuring access to food, medicine and essential goods anytime, anywhere. We design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably. Today, Zipline operates on four continents, makes a delivery somewhere in the world every 30 seconds, and has completed millions of deliveries to date, including blood, vaccines, medical supplies, food, and retail products. Our customers include the world’s largest and most prominent healthcare systems, governments, retailers, restaurants and global businesses who rely on us to save lives, reduce emissions, increase economic opportunity, and provide delivery from point A to point B as fast as possible. The drone is only 15% of what we’ve built to enable seamless, reliable, global operations. Our system strengthens supply chains, reduces congestion, and gives people time back. With more than 140 million commercial autonomous miles safely flown, Zipline is redefining access to healthcare, consumer products, and food across the globe. We operate at a global scale and are looking for practical problem solvers who thrive on real-world challenges and rapid growth. Our team is motivated by building systems that have a direct, meaningful impact on people’s lives and by scaling the future of logistics. We are seeking people who sculpt from first principles, enjoy facing adversity, and can do the impossible at record breaking speeds. About the Role Zipline is hiring a Senior Manager, Data Platform & Autonomy Infrastructure to lead the teams and systems that turn real-world flight data into learning and action. This role owns the end-to-end data platform for autonomy and operations —from onboard logging and ingestion, to postprocessing, sampling, and curated datasets used by autonomy, hardware, operations, and business teams. You will set technical direction, build and lead the organization, and ensure these systems operate reliably at >1 million flights per day with high uptime . This role is a strong fit for leaders who have built large-scale robotics or autonomy data systems in production environments. This role is in person and based in the San Francisco Bay Area . What You’ll Do Set Technical Direction Define the long-term strategy and roadmap for Zipline’s data, autonomy, and ML-enabling infrastructure Establish architectural standards across logging, ingestion, processing, storage, access/visualization, and ML training and evaluation Balance reliability, performance, cost, and developer productivity across the platform Support a diverse set of internal customers, including hardware teams, autonomy/software teams, and analytics/business teams Enable Debugging, Learning, and Scale Support rapid root-cause analysis across autonomy, hardware, and operations Partner with autonomy and validation teams to close the loop between real-world data and development Design systems that scale beyond 1 million flights per day without linear growth in cost or operational complexity Own Autonomy Data, Logging, and Sampling Set direction and accountability for onboard and offboard data logging systems Make principled decisions about what data to collect, retain, and prioritize under bandwidth, storage, and cost constraints Lead development of tooling to identify rare, novel, and safety-relevant scenarios from large-scale flight data Define sampling strategies that maximize signal for autonomy evaluation, simulation, and ML training Build ML Infrastructure Foundations Build and operate infrastructure that supports reproducible training and evaluation for autonomy ML workflows Enable scalable pipelines for dataset generation, experiment tracking, and offline evaluation Establish strong reliability, observability, and operational practices for ML data flows and evaluation runs What You’ll Bring 10+ years of experie
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