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As Head of Quality, AI Data, you will own the systems that turn messy human and model signals into trustworthy, scalable AI data. Our customers’ models are only as good as the data and evaluations we deliver—and as we operate across regions, languages, and time zones, quality cannot be an afterthought or a local optimisation. This role sets the global quality bar. You will define standards, enforce rigor, and build quality systems that scale across distributed teams without slowing delivery. You’ll act as the senior quality leader internally and externally, shaping how customers understand, trust, and use Prolific’s data.
Job Responsibility:
Set the global quality strategy across annotation, human feedback, model evaluation, red-teaming, and multilingual data
Define and enforce quality standards, acceptance criteria, and playbooks across project and pipeline types
Own the quality measurement system, including sampling strategies, agreement metrics, defect taxonomies, and drift detection
Lead a globally distributed quality organisation and design operating rhythms that work asynchronously across regions
Ensure quality is embedded from project kickoff through delivery, including audits, remediation loops, and root-cause analysis
Act as the senior quality partner for customers, clearly explaining methodology, results, risks, and improvement plans
Partner with Product, Engineering, and Operations to embed quality into tooling, workflows, and automation
Hire, develop, and lead quality managers and senior individual contributors, building a culture of rigor and accountability
Requirements:
8–12+ years of experience in quality, data operations, trust and safety, or ML evaluation, with 3+ years leading managers or senior leads
Proven experience running quality programs across globally distributed teams and multiple regions
Strong statistical intuition (sampling, agreement, bias/variance) and comfort working with quality data
Fluency with SQL and dashboards
working knowledge of Python or R for analysis or automation
Deep understanding of human-in-the-loop systems and how data quality impacts model behaviour
Strong judgment balancing speed, cost, and quality, and the ability to make trade-offs explicit
Clear, confident communication with both engineers and executives
Nice to have:
Experience with multilingual or localisation-style QA
Background designing taxonomies or evaluation frameworks tied to model performance
Exposure to regulated or high-scrutiny environments where auditability matters
What we offer:
competitive salary
equity
benefits
remote working
impactful, mission-driven culture
opportunity to earn a cash variable element, such as a bonus or commission