Manager Data Operations & Annotations, Autonomy Data

Zipline•Ann Arbor, MI

2d•$150,000 - $180,000

About The Position

Zipline is seeking a Manager of Data Operations & Annotations to lead the team that turns real-world data into high-quality, cost-effective datasets for its machine learning and autonomy teams. The role spans field operations, autonomy, machine learning, engineering, and data infrastructure. The manager will set strategy for data collection, annotation, and validation, and build an operation that continually improves quality, coverage, speed, and economics. The role also leads and develops the organization supporting these systems and partners closely with technical teams so data operations evolve with the needs of Zipline’s autonomy stack. Success means delivering the right data to ML teams faster, at higher quality and lower cost; making collection more deliberate and annotation and processing more efficient; and translating model needs into field action quickly.

Requirements

Experience leading and scaling operational or technical teams, including developing managers.
Experience owning technically complex operational systems and improving their performance at scale.
Strong systems thinking and technical judgment across people, process, hardware, software, and infrastructure.
Experience leading ambiguous, cross-functional work from problem definition through sustained operation.
Strong judgment in balancing quality, throughput, cost, and reliability.
A track record of using metrics, tooling, and automation to drive measurable operational improvements.
Clear communication and the ability to drive alignment and decisions across technical and operational teams.

Nice To Haves

Experience designing or operating large-scale data labeling or annotation programs.
Experience managing external vendors or distributed workforces supporting data operations.
Experience with machine learning, autonomy, robotics, aerospace, or other sensor-rich physical systems.
Familiarity with the ML data lifecycle, including data collection, sampling, annotation, validation, dataset generation, and model feedback loops.
Experience translating model performance gaps into targeted real-world data collection.
Familiarity with multimodal datasets, sensor data, telemetry, or logging systems.

Responsibilities

Lead the organization and end-to-end operations that collect, annotate, validate, and deliver real-world data at the scale, speed, and cost required for ML and autonomy development.
Partner with ML and autonomy teams to translate model needs into data requirements, collection strategies, and operational priorities.
Design and improve annotation, validation, and quality-control workflows using tooling, automation, and metrics to optimize quality, coverage, speed, and cost.
Develop managers and teams, establish clear ownership, and build a culture of accountability and continuous improvement.
Lead cross-functional programs and drive decisions across operations, engineering, ML, and autonomy.
Use operational data and feedback to identify bottlenecks and drive automation or engineering improvements that increase scale without proportional growth in manual effort or cost.