AI Data Quality

AI Data Quality & Engineering Lead at TaskUs

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 fast-growing sectors, including social media, e-commerce, gaming, streaming media, food delivery, ride-sharing, HiTech, FinTech, and HealthTech.

People First culture has grown the company to have approximately 45K employees worldwide. We are currently in twenty-three locations across twelve countries, including the Philippines, India, and the United States. We aim to create growth for our partners through a culture of constant motion, exploring new technologies, and mastering consistency in an ever-changing world.

What we offer : TaskUs provides world-class benefit packages with competitive salaries. We promote internal mobility and professional development and support a People First culture. We encourage inclusiveness and community impact.

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 are 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 speed or efficiency.

The impact you’ll make

• Build and guide a high-performing team: Lead and mentor Data Quality Analysts, set clear quality goals, provide feedback, and foster a culture of precision and accountability.
• Ensure quality at scale: Develop and refine robust QA processes, SOPs, and statistical quality metrics (e.g., F1 score, inter-annotator agreement) to protect annotation integrity.
• Drive transparency and insight: Create dashboards and reports that reveal quality trends, root causes of errors, and improvement opportunities; communicate insights to leadership and clients.
• Champion tool innovation and efficiency: Manage annotation and QA platforms (e.g., Labelbox, Dataloop, LabelStudio) and evaluate or implement new automation tools to elevate efficiency and maintain quality.

Key Responsibilities

Strategic Leadership

• Develop, refine, and document quality assurance processes and SOPs to ensure high-quality outputs.
• Establish quality metrics (e.g., F1 score, inter-annotator agreement) that align with business objectives and industry standards.
• Continuously review annotation workflows to identify risks and opportunities to increase efficiency and reduce errors.
• Act as a subject matter expert on annotation quality, providing ongoing feedback, training, and support to annotators and project teams.

Analysis & Reporting

• Lead in-depth data analysis to diagnose quality issues, assess strategy effectiveness, and uncover root causes of recurring errors.
• Develop dashboards for real-time quality metrics and project performance.
• Prepare and deliver strategic quality reports to senior management and clients, outlining quality trends, risks, and improvement plans.
• Collaborate with cross-functional teams to align on project goals and QA initiatives.

Operational Leadership

• Lead a team of Data Quality Analysts, providing mentorship and training to foster continuous improvement and accountability.
• Manage configuration and integration of annotation and quality control tools (e.g., Labelbox, Dataloop, LabelStudio) to ensure optimal performance.
• Identify and implement innovative quality control tools and automation technologies to streamline workflows and improve efficiency.

Required Qualifications

• 3+ years in data quality management, data operations, or related AI/ML or data annotation roles.
• Proven experience designing and executing QA strategies for large-scale, multi-modal data annotation projects.
• Leadership experience managing remote or distributed teams.
• Deep understanding of data annotation processes, QA methodologies, and metrics (e.g., F1 score, inter-annotator agreement).
• Strong data-analysis skills and ability to translate findings into actionable recommendations.
• Excellent communication skills with experience presenting data and quality insights to technical and non-technical stakeholders.
• Proficiency with annotation and QA tools (e.g., Labelbox, Dataloop, LabelStudio).
• High proficiency in Excel and Google Sheets; familiarity with Python and SQL.
• Familiarity with AI/ML pipelines concepts including data prep, model training, and evaluation.
• Can start ASAP or within 30 days.

Preferred Qualifications

• Experience in agile or fast-paced tech environments with AI/ML pipelines.
• Experience in managed services or vendor-driven settings.
• Familiarity with prompt engineering and LLM-assisted workflows for annotation and validation.
• Knowledge of ethical AI practices and compliance frameworks.

Education / Certifications

• Bachelor’s degree in a technical field (e.g., Computer Science, Data Science) or equivalent professional experience.

Why you’ll love this role

• Remote-first flexibility: Work from anywhere you’re most productive.
• New challenges: Work on first-of-their-kind problems at the frontier of AI safety.
• Visible impact: Your architectures influence how AI teams mitigate risk.
• Room to grow: Build the solutions function from scratch and scale into leadership as revenue grows.
• Mission-driven team: Collaborate with sellers, researchers, and engineers focused on safer AI.

Work Location / Work Schedule

• Remote-friendly; on-site if needed
• Day or Mid Shift, Philippine Time (PHT)
• Open to PH sites

DEI & Compliance

TaskUs values equal opportunity and inclusive practices. If you need reasonable accommodations in any part of the hiring process, please let us know.

We invite you to explore TaskUs career opportunities and apply through the provided URL: .