AI Data Operations Manager
TaskUs
full-time
Posted on:
Location Type: Remote
Location: Ireland
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About the role
- Own the day-to-day strategy and operations for human data annotation projects that power AI and ML pipelines.
- Manage distributed teams, keeping workflows running smoothly, and ensuring high quality labelled data is delivered on time and to spec.
- Lead end to end delivery of data annotation projects across multiple data types and use cases, ensuring agreed volumes, timelines and SLAs are met.
- Plan headcount, schedules and coverage across sites and remote workers based on forecast demand and priority work, updating plans as requirements change.
- Monitor core operational metrics such as throughput, turnaround time, utilisation and rework.
- Flag risks early, manage incidents and escalations, and coordinate corrective actions to prevent repeat issues.
- Partner closely with the Quality team for realistic sampling plans, audits and evaluation designs, ensuring insights translate into improvements to workflows, coaching and guidelines.
- Work with research, engineering and other client stakeholders to translate complex project/model requirements into high-quality data pipelines.
- Be the go-to expert on human data and annotation operations, providing clear guidance on best practices and shaping how data is annotated across the team.
- Use operational and quality insights to refine annotation schemas, guidelines and operational workflows over time.
- Act as a primary operational contact for clients and key internal stakeholders, owning regular business reviews and sharing updates on changes, risks, and timelines.
- Maintain clear, accurate SOPs and documentation to support operational consistency, knowledge transfer and scalability.
- Manage a distributed team, set clear expectations, give regular feedback and run structured performance and development conversations.
- Shape onboarding, calibration and upskilling plans in collaboration with Learning Experience and Quality teams to meet client expectations.
- Create a feedback loop for team leads and annotators to surface challenges and ideas, continuously refining guidelines and workflows.
Requirements
- Bachelor’s degree in a relevant field (for example linguistics, social sciences, humanities, computer science) or equivalent practical experience.
- 4+ years in operations, programme or project management, with at least 2-3 years directly running data annotation, data labelling or content review operations for AI or ML products.
- Proven experience in designing and owning high-quality annotation pipelines for AI/ML workflows.
- Experience leading distributed or remote teams.
- Strong familiarity with data annotation pipelines and how quality, sampling, audits and reviewer guidance affect performance.
- Hands-on experience with annotation or labelling tools and workflows, ideally across more than one data type (for example text, images, audio or video).
- Comfortable working with operational metrics and using data to understand performance, explain performance and make decisions.
- Comfortable operating in a fast-moving, sometimes ambiguous environment, and able to prioritise and move work forward without perfect information.
- Evidenced ability to use data and structured thinking to improve processes, not just operate them.
- Strong written and verbal communication skills in English, able to communicate clearly with annotators, internal stakeholders and external clients.
Benefits
- Competitive industry salaries
- Comprehensive benefits packages
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data annotationdata labellingcontent reviewannotation pipelinesoperational metricsdata analysisproject managementquality assuranceworkflow optimizationperformance metrics
Soft skills
leadershipcommunicationteam managementproblem-solvingadaptabilitycollaborationfeedback managementstrategic planningcoachingcritical thinking