AI Data Quality & Engineering Lead

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.

What can you expect in an AI Data Quality & Engineering Lead role with TaskUs:

Why this role exists:
As AI systems scale rapidly across industries, the integrity and accuracy of testing, training, and evaluation data have never been more critical. TaskUs needs a proactive leader who can architect and uphold high‑quality annotation workflows so that AI models are built and evaluated on reliable data without compromising on speed or efficiency.

The impact you’ll make:

• Build and guide a high-performing team: Lead and mentor a team of Data Quality Analysts, setting clear quality goals, delivering feedback, and fostering a culture of precision and accountability.

• Ensure quality at scale: Develop and continually refine robust QA processes, SOPs, and statistical quality metrics (e.g., F1 score, inter‑annotator agreement) to protect the integrity of annotation outputs. 

• Drive transparency and insight: Create dashboards and reports that reveal quality trends, root causes of errors, and improvement opportunities - communicating these insights to leadership and clients.

• Champion tool innovation and efficiency: Manage annotation and QA platforms (like Labelbox, Dataloop, LabelStudio), and lead the evaluation or implementation of new automation tools to elevate efficiency and maintain quality.

Responsibilities:
- Strategic Leadership

• Drive the development, refinement, and documentation of quality assurance processes and standard operating procedures to ensure high-quality outputs.

• Establish comprehensive quality metrics (e.g. F1 score, inter-annotator agreement) that align with business objectives and industry standards.

• Continuously review and refine annotation workflows to proactively identify risks and areas to increase efficiency and reduce errors.

• Act as the subject matter expert on annotation quality, providing ongoing feedback, training, and support to annotators and project teams to uphold the highest quality standards.

- Analysis & Reporting

• Lead in-depth data analysis to diagnose quality issues, assess the effectiveness of quality strategies, and uncover root causes of recurring errors.

• Develop and maintain dashboards that provide real-time insights into quality metrics and project performance.

• Prepare and deliver strategic quality reports to senior management and clients, articulating quality trends, risks, and improvement plans.

• Partner with cross-functional teams, including operational management, engineering, and client services, to align on project goals and quality assurance initiatives.

- Operational Leadership

• Lead a team of Data Quality Analysts and provide mentorship, training, and expertise, fostering a culture of continuous improvement and accountability.

• Manage