Data Quality Engineer, AI Business

Prolific•Seattle, WA

About The Position

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.

Requirements

5+ years of experience in quality engineering, data or annotation quality, analytics engineering, trust and integrity, or ML/LLM evaluation operations.
Strong proficiency in Python and SQL, with comfort applying statistical concepts such as sampling strategies, confidence levels, and agreement metrics.
A proven track record of turning ambiguous or messy quality problems into clear metrics, automated checks, and durable process improvements.
Strong quality systems thinking, with the ability to translate complex edge cases into clear rules, tests, rubrics, and governance mechanisms.
Hands-on experience instrumenting workflows and implementing pragmatic automation that catches quality and integrity issues early.
Demonstrated ability to influence cross-functional teams (Product, Engineering, Operations, Client teams) and drive change without direct authority.
Strong customer empathy, with a clear understanding of what “useful, trustworthy data” means for research, AI training, and evaluation use cases.

Nice To Haves

Familiarity with data collection mechanics (screeners, quota/routing constraints, study design patterns).
LLM evals, red teaming, or policy-based annotation experience.
Data/versioning discipline (dataset lineage, change control, reproducibility).
Experience with integrity/fraud detection systems and anti-abuse tooling

Responsibilities

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.
Write and continuously improve guidelines and training materials to keep participants, reviewers, and internal teams aligned on evolving quality standards.
Investigate quality and integrity issues end to end, running root-cause analysis across guidelines, UX, screening, training, and operations, and driving corrective and preventive actions (CAPAs).
Build dashboards and operating cadences to track defect rates, rework, throughput versus quality trade-offs, integrity events, and SLA adherence.
Lead calibration sessions and coach QA leads and reviewers to improve decision consistency, rubric application, and overall quality judgement.
Translate one-off quality fixes into repeatable, scalable playbooks across customers, programs, and study types.