Data Quality Engineer, AI Business 

Team: Client Services

 

Prolific

Prolific isn’t just enabling AI innovation – we’re redefining it. While foundational AI technologies are becoming commoditized, Prolific’s human data infrastructure provides the high-quality, diverse data required to train the next generation of AI models. Through our platform, we empower researchers and companies to access a global, ethically curated participant base, ensuring cutting-edge AI research and training grounded in inclusivity and precision.

 

The Role

As a Data Quality Engineer within Prolific AI Data Services, you will be the quality guardian for our managed service studies. You will design and operationalise the measurement systems, automation, and launch gates that ensure the data we deliver is trustworthy, authentic, and scalable.
This role sits at the intersection of data quality, automation, and integrity. You’ll work closely with Product, Engineering, Operations, and Client teams to embed quality and authenticity into study design and execution—enabling faster launches without compromising trust as task types and volumes evolve.

 

What You’ll Be Doing

• Own end-to-end quality design for Prolific managed service studies, including rubrics, acceptance criteria, defect taxonomies, severity models, and clear definitions of done.
• Define, implement, and maintain quality measurement systems, including sampling plans, golden sets, calibration protocols, agreement targets, adjudication workflows, and drift detection.
• Build and deploy automated quality checks and launch gates using Python and SQL, such as schema and format validation, completeness checks, anomaly detection, consistency testing, and label distribution monitoring.
• Design and run launch readiness processes, including pre-launch checks, pilot calibration, ramp criteria, full-launch thresholds, and pause/rollback mechanisms.
• Partner with Product and Engineering to embed in-study quality controls and authenticity checks into workflows, tooling, and escalation paths.