Senior Manager, Data Platform & Autonomy Infrastructure

Zipline•South San Francisco, CA

12h•$225,000 - $275,000•Onsite

About The Position

Zipline is the world’s largest and most experienced drone delivery service, on a mission to serve all humans equally by ensuring access to food, medicine and essential goods anytime, anywhere. They design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably. Zipline operates on four continents, makes a delivery every 30 seconds, and has completed millions of deliveries including blood, vaccines, medical supplies, food, and retail products. Their customers include prominent healthcare systems, governments, retailers, restaurants, and global businesses who rely on them to save lives, reduce emissions, increase economic opportunity, and provide fast delivery. The drone is only 15% of their system, which strengthens supply chains, reduces congestion, and gives people time back. With over 140 million commercial autonomous miles safely flown, Zipline is redefining access to healthcare, consumer products, and food globally. They operate at a global scale and seek practical problem solvers who thrive on real-world challenges and rapid growth, motivated by building systems with direct, meaningful impact and scaling the future of logistics. They look for individuals who sculpt from first principles, enjoy facing adversity, and can achieve the impossible at record-breaking speeds.Zipline is hiring a Senior Manager, Data Platform & Autonomy Infrastructure to lead the teams and systems that transform real-world flight data into learning and action. This role encompasses the end-to-end data platform for autonomy and operations, from onboard logging and ingestion to postprocessing, sampling, and curated datasets utilized by autonomy, hardware, operations, and business teams. The Senior Manager will define technical direction, build and lead the organization, and ensure these systems maintain reliable operation at over 1 million flights per day with high uptime. This position is ideal for leaders with experience building large-scale robotics or autonomy data systems in production environments. This role is in person and based in the San Francisco Bay Area.

Requirements

10+ years of experience building large-scale data, infrastructure, or autonomy-adjacent systems
Experience leading senior engineers or multiple teams
Strong background in robotics, autonomy, logistics, or other data-intensive physical systems
Deep understanding of logging, ingestion, processing, and data platform architecture
Experience working in environments where data is safety-critical, expensive, and operationally constrained
Strong communication skills and sound technical judgment

Responsibilities

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
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
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 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