AI Data Quality and Engineering Lead

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 the configuration and integration of annotation and quality control tools (e.g. Labelbox, Dataloop, LabelStudio), ensuring optimal tool performance and alignment with project requirements
• Identify, evaluate, and implement innovative quality control tools and automation technologies to streamline quality control workflows, enhance analytical capabilities, and improve operational efficiency.

Required Qualifications

• Bachelor’s degree in a technical field (e.g. Computer Science, Data Science) or equivalent professional experience.
• 3+ years of experience in data quality management, data operations, or related roles within AI/ML or data annotation environments.
• Proven track record in designing and executing quality assurance strategies for large-scale, multi-modal data annotation projects.
• Proven track record in a leadership role managing and developing high-performing, remote or distributed teams.
• Deep understanding of data annotation processes, quality assurance methodologies, and statistical quality metrics (e.g., F1 score, inter-annotator agreement).
• Strong data-analysis skills, with the ability to interrogate large datasets, perform statistical analyses, and translate findings into actionable recommendations.
• Excellent communication skills, with experience presenting complex data and quality insights to technical and non-technical stakeholders.
• Proficiency with annotation and QA tools (e.g., Labelbox, Dataloop, LabelStudio).
• High-level of proficiency in common data-analysis tools, such as Excel and Google Sheets.
• Familiarity with programmatic data analysis techniques (e.g. Python, SQL).
• Familiarity with the core concepts of AI/ML pipelines, including data preparation, model training, and evaluation.

Preferred Qualifications

• Prior experience in an agile or fast-paced tech environment with exposure to AI/ML pipelines.
• Experience in a managed services or vendor-driven environment.
• Familiarity with prompt engineering and large-language-model assisted workflows to optimise annotation and validation processes.
• In-depth knowledge of ethical AI practices and compliance frameworks.