Manager Data Operations & Annotations, Autonomy Data

Job description

About this 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.

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.

Qualifications and 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.
• 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.