Manager Data Operations & Annotations, Autonomy Data

Zipline; Detroit (MI); On-site; USD 150,000 - 180,000; Full time

Job summary

Zipline seeks a leader to build and scale data-collection operations for ML and autonomy. You will define how data is gathered, annotated, and validated, and grow the organization to support rapid, cost-efficient data production across field operations and engineering teams.

You will partner with ML, autonomy, and data-infra teams to optimize data workflows, improve quality and coverage, and drive automation for scalable performance.

Qualifications

• 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.
• Experience designing or operating large-scale data labeling or annotation programs.

Responsibilities

• 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.

Skills

Leadership & scaling

Operational systems ownership

Systems thinking

Cross-functional leadership

Data operations lifecycle

Metrics & automation

Communication & alignment

Job description

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.

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.

About You And The Role

You will lead the team responsible for turning real-world data into high-quality, cost-effective datasets for Zipline’s machine learning and autonomy teams. This role spans field operations, autonomy, ML, engineering, and data infrastructure. You will set the strategy for how data is collected, annotated, and validated, while building an operation that continuously improves its quality, coverage, speed, and economics. You will also lead and develop the organization behind these systems, while partnering closely with technical teams to ensure data operations evolve with the needs of our autonomy stack.

What You’ll Do

• 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.

What You’ll Bring

• 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.

What Will Make You Stand Out

• 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.

What Success Looks Like

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.

What Else You Need To Know

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.

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self‑identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.

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.