Senior Manager, Data Platform & Autonomy Infrastructure

Zipline•South San Francisco, CA

5d•$225,000 - $275,000•Onsite

About The Position

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. This role owns how Zipline learns from the real world. The Senior Manager, Data Platform & Autonomy Infrastructure builds the systems that turn millions of flights into faster autonomy improvement, safer operations, and a scalable, profitable business. If you want to work on data systems at the intersection of robotics, autonomy, and real-world impact—this is a rare opportunity.

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

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