AI Data Operations Manager

About TaskUs

TaskUs is 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, TaskUs serves clients in the fastest-growing sectors, including social media, e-commerce, gaming, streaming media, food delivery, ride-sharing, HiTech, FinTech, and HealthTech.

The People First culture at TaskUs has enabled the company to expand its workforce to approximately 45,000 employees globally. Presently, we have a presence in twenty-three locations across twelve countries, which include the Philippines, India, and the United States.

It started with one ridiculously good idea to create a different breed of Business Processing Outsourcing (BPO)! We at TaskUs understand that achieving growth for our partners requires a culture of constant motion, exploring new technologies, being ready to handle any challenge at a moment's notice, and mastering consistency in an ever-changing world.

What We Offer

At TaskUs, we prioritize our employees' well-being by offering competitive industry salaries and comprehensive benefits packages. Our commitment to a People First culture is reflected in the various departments we have established, including Total Rewards, Wellness, HR, and Diversity. We take pride in our inclusive environment and positive impact on the community. Moreover, we actively encourage internal mobility and professional growth at all stages of an employee's career within TaskUs. Join our team today and experience firsthand our dedication to supporting People First.

About The Role: AI Data Operations Manager

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.

Responsibilities

Operational Leadership

• 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.

Strategic Leadership

• 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.

People Leadership

• 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.

Required Qualifications

• 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.
• Comfortable working with global, multicultural teams.

Preferred Qualifications

• Experience supporting data operations across the AI/ML lifecycle such as data annotation, data collection, or evaluation of models.
• Background in a data provider, AI/ML company, or platform environment working in human-annotated data.
• Experience working with or coordinating external vendors, freelance contributors or crowd platforms.
• Working knowledge of SQL or a scripting language such as Python for deeper operational analysis and validation of metrics.
• Experience helping to launch new data or annotation projects from early-stage pilots through to scaled operations.
• Working understanding of AI/ML data concepts such as RLHF and SFT.
• Familiarity with LLMs and using them to automate workflows.

Key skills/competency

• Data Operations
• AI/ML Pipelines
• Data Annotation
• Project Management
• Team Leadership
• Process Improvement
• Client Management
• Operational Metrics
• Quality Assurance
• Distributed Teams