Human Data Quality Engineer (Founding Team)

Prolific•New York, NY

23d•Remote

About The Position

As one of the founding members of Prolific's newly formed AI Data Services team, you'll help build the quality systems behind some of the world's most advanced AI models. Data quality is a strategic priority for Prolific, so this is a high-visibility role with direct exposure to senior stakeholders. This isn't a traditional QA role. We are not looking for someone to review data against a predefined checklist. We are looking for an innovative thinker that can leverage their expertise to define what good means where no definition exists yet. Acting as a strategic thought partner, you’ll work at the intersection of human data, machine learning, evaluation across frontier use-cases that define what high-quality human data looks like for the next generation of advanced AI. This means that much of the work involves novel problems with no established answer, so you’ll be comfortable working through ambiguity. Your primary focus is working directly with clients and alongside frontier AI labs, translating what their models need into robust human data and evaluation strategies. Rather than checking quality at the end of a project, you'll engineer quality into every stage of the lifecycle, from study design and participant strategy through to evaluation, launch readiness and client delivery. You will also work alongside our product engineering, and supply teams to define and build the quality infrastructure that will enable us to deliver high quality human data at scale. Much of the work you'll tackle won't have an existing playbook. You'll help create it.

Requirements

5+ years of experience in building quality, evaluation or annotation systems within AI, machine learning, LLMs or human data environments.
Strong Python and SQL skills, with a passion for using data to solve complex quality problems.
A solid understanding of machine learning pipelines and how human data impacts model performance.
Strong analytical and statistical thinking, with experience designing scalable quality frameworks.
The confidence and credibility to interact with stakeholders at frontier labs and act as a partner.
The ability to leverage your experience and expertise to influence and guide stakeholders at every level, both client side and internally.
The ability to turn your own data analysis and quality methodology into requirements that product and engineering can build into systems.
The ability to explain your data analysis and findings clearly to non-technical stakeholders, so they can act on them.
A proactive, builder's mindset - you enjoy creating new systems, navigating ambiguity and improving how things work.

Nice To Haves

Experience with LLM evaluation, RLHF, AI safety or red teaming.
Experience translating vague model or evaluation goals into clear annotation specifications.
Experience working with human annotation programmes or human data operations.
Familiarity with calibration, inter-rater agreement, drift detection or other evaluation methodologies.

Responsibilities

Design the quality frameworks that underpin complex human data programmes, from evaluation rubrics through to launch readiness.
Work with clients as a strategic thought partner, challenging annotation schemas and data requirements when they won't produce the signal the model needs.
Advise on project design and how the choice of schema can impact data quality.
Build quality upstream across the operational workflow, from recruitment, screening, and training through to writing guidelines and running calibration sessions.
Build scalable quality systems, measurement frameworks and automated checks using Python and SQL.
Partner with product and engineering to build the quality infrastructure that delivers high-quality human data at scale.
Investigate data quality and integrity issues, identifying root causes and turning insights into scalable improvements.
Architect and build dashboards, monitoring and reporting that provide clear visibility into quality and operational performance.
Raise the quality capability across the company, upskilling operations and acting as a thought mentor to junior analysts.
Help define how Prolific approaches quality across new AI domains, shaping best practice as the team grows.