Manager Data Operations & Annotations, Autonomy Data

Role overview

This leadership role owns the end-to-end pipeline that turns real-world operational data into high-quality datasets for machine learning and autonomy development. The position sits at the intersection of field operations, engineering, ML, and data infrastructure, setting strategy while building and growing the team that executes it. The work is fundamentally about scaling data operations to match the pace, quality, and cost discipline needed for a rapidly evolving autonomy stack.

Responsibilities

- Lead the organization responsible for collecting, annotating, validating, and delivering real-world data at the scale, speed, and cost required for ML and autonomy development. - Partner with ML and autonomy engineering teams to convert model requirements into data collection strategies and operational priorities. - Design and continuously improve annotation, validation, and quality-control workflows using tooling, automation, and measurable metrics. - Develop managers and individual contributors, establish clear ownership, and build a culture of accountability and continuous improvement. - Drive cross-functional programs and decision-making across operations, engineering, ML, and autonomy stakeholders. - Use operational data and feedback loops to identify bottlenecks and push for automation or engineering improvements that increase scale without proportional growth in manual effort or cost.

Requirements

- Demonstrated experience leading and scaling operational or technical organizations, including developing managers. - Track record owning technically complex operational systems and improving their performance at scale. - Strong systems thinking and technical judgment spanning people, process, hardware, software, and infrastructure. - Experience leading ambiguous, cross-functional initiatives from problem definition through sustained operation. - Sound judgment in balancing quality, throughput, cost, and reliability trade-offs. - Clear communication and the ability to drive alignment across technical and operational teams.

Nice to have

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

Benefits and work setup

- Target starting cash range of $150,000-$180,000, with final compensation dependent on experience, qualifications, skills, work location, and projected impact. - Total compensation package may also include equity, overtime pay, discretionary annual or performance bonuses, sales incentives, medical, dental, and vision insurance, paid time off, and additional benefits. - The employer is an equal opportunity employer that prohibits discrimination based on protected characteristics and welcomes applications from candidates traditionally underrepresented in technology.