Manager Data Operations & Annotations, Autonomy Data

Lead the organization and end-to-end operations that collect, annotate, validate, and deliver high-quality 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.

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.

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.

The right data reaches ML teams faster, at higher quality and lower cost. Data collection becomes increasingly deliberate, annotation and processing scale efficiently, and model needs translate quickly into action in the field. Ultimately, you will own a critical part of how Zipline learns from the real world.

The starting cash range for this role is $150,000 - $180,000. Please note that this is a target, starting cash range for a candidate who meets the minimum qualifications for this role. The final cash pay for this role will depend on a variety of factors, including a specific candidate's experience, qualifications, skills, working location, and projected impact. The total compensation package for this role may also include: equity compensation; overtime pay; discretionary annual or performance bonuses; sales incentives; benefits such as medical, dental and vision insurance; paid time off; and more.

Zipline is an equal opportunity employer and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws or our own sensibilities.

We value diversity at Zipline and welcome applications from those who are traditionally underrepresented in tech. If you like the sound of this position but are not sure if you are the perfect fit, please apply!

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As set forth in Zipline ’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.