Operations Lead – AI Data Operations

The Role:

We are seeking an Operations Lead who will take ownership of our data operations and help build the operational foundation required to support complex AI data programs.

In this role, you will oversee the execution and delivery of annotation programs while designing the workflows, quality systems, and operational frameworks needed to scale effectively. You will work closely with delivery teams, project managers, QA teams, and leadership to ensure projects are executed efficiently and consistently. Acting at the center of our delivery infrastructure, you will translate client requirements into operational workflows, coordinate global contributor teams, and continuously improve the systems that support project delivery.

Strong communication and stakeholder management skills are essential, as this role regularly coordinates between clients, delivery teams, and leadership to keep projects aligned and running smoothly. Our work often involves running multiple pilot datasets and experimental workflows in parallel before scaling into larger production programs, so the ability to manage several concurrent projects and adapt quickly to changing priorities is critical.

Responsibilities:

• Operational Leadership

  • Own the execution and delivery of AI data labeling programs across multiple clients and datasets.

  • Build and maintain operational systems that support scalable project delivery.

  • Establish operational frameworks that ensure consistent quality, efficiency, and throughput.

  • Identify operational bottlenecks and implement improvements that increase productivity and accuracy.

  • Design operational playbooks for onboarding new projects and scaling existing ones.

• Program & Project Delivery

  • Oversee the full lifecycle of annotation programs from onboarding through delivery.

  • Manage a portfolio of concurrent projects including pilot datasets, experimental annotation workflows, and larger production programs.

  • Coordinate across project managers, QA teams, and contributor networks to ensure timelines and quality standards are met.

  • Monitor operational performance and proactively address delivery risks or capacity challenges.

  • Ensure project requirements, guidelines, and operational processes are clearly defined for delivery teams.

• Data Operations & Workflow Design

  • Design and refine annotation workflows that support complex and multimodal datasets.

  • Implement scalable quality assurance frameworks across annotation pipelines.

  • Develop operational processes that support different dataset modalities including image, video, sensor, spatial, and language data.

  • Build repeatable operational playbooks for new dataset types and project structures.

• Client Delivery Coordination

  • Support the operational onboarding of new client programs.

  • Translate client requirements into actionable workflows for internal teams.

  • Maintain alignment between client expectations and delivery capabilities.

  • Provide clear operational updates that support strong client relationships.

• Metrics & Performance Management

  • Track key operational metrics including throughput, quality performance, delivery timelines, and operational efficiency.

  • Maintain internal dashboards providing visibility into project health and operational performance.

  • Use operational insights to continuously improve workflows, quality systems, and delivery processes.

• Team Development & Coordination

  • Guide and coordinate project managers, QA teams, and distributed contributor networks.

  • Establish operational best practices for managing global delivery teams.

  • Support the development of operational structures that allow data operations to scale effectively.

Requirements:

• 6–10 years of experience in operations, program management, or delivery roles, with direct experience in AI data services, data labeling, or data annotation companies.

• Experience managing large-scale annotation programs involving distributed teams, contributor networks, and structured quality frameworks, while handling multiple concurrent projects, pilots, or experimental datasets.

• Familiarity with multimodal datasets such as image, video, spatial, sensor, or language data, and experience designing or improving annotation workflows and operational processes.

• Excellent written and verbal communication skills with the ability to coordinate effectively with clients, internal teams, and external partners across multiple geographic regions.

• Strong understanding of how labeled datasets are used in machine learning pipelines, along with experience using operational and project management tools such as ClickUp, Jira, Notion, Airtable, or similar platforms.

• Comfortable working with globally distributed teams and supporting delivery environments that may require coordination across time zones or shift schedules.

• Resourceful, adaptable, and comfortable operating in fast-moving environments while guiding teams, improving processes, and helping build the operational foundation of a growing company.