Manager, Data Operations and Annotations

This is a fully remote position, open to applicants in Michigan.

📋 Description

• Oversee the organization and comprehensive operations that gather, annotate, validate, and provide high-quality real-world data at the scale, speed, and cost necessary for machine learning and autonomy development.

• Collaborate with machine learning and autonomy teams to convert model requirements into data specifications, collection strategies, and operational priorities.

• Design and enhance workflows for annotation, validation, and quality control, utilizing tools, automation, and metrics to optimize quality, coverage, speed, and cost.

• Develop managers and teams, establish defined ownership, and cultivate a culture of accountability and continuous enhancement.

• Lead cross-functional initiatives and influence decisions across operations, engineering, machine learning, and autonomy.

• Leverage operational data and feedback to identify bottlenecks and implement automation or engineering improvements that enhance scale without a corresponding increase in manual effort or cost.

• Own a crucial aspect of how Zipline learns from real-world experiences.

⛳️ Requirements

• Proven experience in leading and scaling operational or technical teams, including the development of managers.

• Demonstrated ability to own technically complex operational systems and enhance their performance at scale.

• Strong systems thinking and technical judgment across personnel, processes, hardware, software, and infrastructure.

• Experience in managing ambiguous, cross-functional tasks from problem identification through to sustained operation.

• Strong judgment in balancing quality, throughput, cost, and reliability.

• A history of leveraging metrics, tools, and automation to achieve measurable operational improvements.

• Excellent communication skills with the ability to foster alignment and drive decisions across technical and operational teams.

• Experience in designing or managing large-scale data labeling or annotation initiatives.

• Experience in overseeing external vendors or distributed workforces that support data operations.

• Familiarity with machine learning, autonomy, robotics, aerospace, or other sensor-rich physical systems.

• Understanding of the machine learning data lifecycle, including data collection, sampling, annotation, validation, dataset generation, and model feedback loops.

• Experience in translating model performance deficiencies into focused real-world data collection efforts.

• Knowledge of multimodal datasets, sensor data, telemetry, or logging systems.

🏝️ Benefits

• Equity compensation

• Overtime pay

• Discretionary annual or performance bonuses

• Sales incentives

• Medical, dental, and vision insurance

• Paid time off