AI Data Operations Manager

Location: Remote (Applicants must be based in UK, Ireland, Greece, or Croatia
)

Role Type: Full-time

Task Us are a provider of outsourced digital services and next-generation customer experience to fast-growing technology companies, helping its clients represent, protect and grow their brands. Leveraging a cloud-based infrastructure, Task Us serves clients in the fastest-growing sectors, including social media, e-commerce, gaming, streaming media, food delivery, ride-sharing, HiTech, Fin Tech, and Health Tech.

The People First culture at Task Us has enabled the company to expand its workforce to approximately 65,000 employees globally. Presently, we have a presence in twenty-three locations across twelve countries, which include the Philippines, India, and the United States.

You will own the day-to-day strategy and operations for human data annotation projects that power AI and ML pipelines. This includes managing geographically distributed teams, keeping workflows running smoothly, and ensuring we deliver high quality labelled data on time and to spec.

You will bring deep experience of human data operations and your understanding of what good training data looks like, and use that knowledge to shape how we work. You will partner closely with our Quality team, Solutions, Community, and client stakeholders to improve processes, raise standards, and align operations with model and client goals.

• 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 so capacity reflects 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, coordinate corrective actions and prevent repeat issues.
• Partner closely with the Quality team so that sampling plans, audits and evaluation designs are realistic and well resourced, and ensure their insights translate into improvements to workflows, coaching and guidelines.
• Partner closely with the Recruitment and Training teams to ensure your team’s skillset match the client requirements.

• 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 helping shape how data is annotated across the team.
• Use operational and quality insights to shape how the project evolves over time, refining annotation schemas, guidelines and operational workflows.
• Act as a primary operational contact for clients and key internal stakeholders, owning regular business reviews, sharing key updates, and aligning 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.
• Work with Learning Experience and Quality teams to shape onboarding, calibration and upskilling plans so teams can take on new tasks and deliver to client expectations.
• Create a feedback loop where team leads and annotators can surface challenges and ideas from the front line, helping continuously refine guidelines and workflows.

• 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…