Data Quality Engineer

IHRE AUFGABEN We are looking for a Data Quality & Benchmarking Engineer to strengthen the quality process of our DataPilot product and related data/ML platform components. This role is embedded within the development team and is responsible for ensuring that features are validated early, tested against realistic datasets, benchmarked properly, and aligned with product requirements and technical designs before they reach final product acceptance. The mission is not only to find bugs at the end of the development cycle. The mission is to improve how we define, verify, measure, and release quality throughout the development process. You will help us reduce the gap between product requirements, technical design, and final implementation by introducing structured validation, dataset-based regression testing, benchmarking practices, and exploratory testing for scenarios that cannot be fully automated. Responsibilities As a Data Quality & Benchmarking Engineer, you will: * Review product requirements, technical designs, and acceptance criteria before and during implementation. * Translate PRDs and technical designs into clear validation scenarios, edge cases, and test plans. * Verify that implemented features match the agreed product behavior and technical design. * Define and maintain test datasets for our platforms, including golden datasets, dirty datasets, large datasets, edge-case datasets, and regression datasets. * Validate data processing behavior, data quality checks, labeling flows, correction flows, exports, and user-facing results. * Design and execute benchmark scenarios for our timeseries platform features and Kubeflow pipelines. * Measure runtime, memory usage, throughput, scalability limits, failure behavior, and regression between releases. * Perform exploratory testing for complex user flows, realistic data scenarios, and cases that are hard or inefficient to automate. * Identify which validation checks should become automated tests and collaborate with developers to implement them. * Support release decisions by providing clear quality findings, benchmark results, and risk assessments. * Work closely with Product, Backend, Frontend, Data, MLOps, and Platform engineers to improve the overall quality process. * Help define and improve the team’s Definition of Done, engineering acceptance process, and quality gates. * Document test scenarios, dataset assumptions, benchmark results, and known limitations.   What success looks like Success in this role means that: * Product receives features that have already been validated against the PRD and design. * Fewer issues are discovered late during product acceptance. * DataPilot has a structured catalog of test datasets and regression scenarios. * Benchmark results are available for important features and pipeline changes. * Performance or data-quality regressions are detected before release. * Developers receive earlier feedback during implementation. * Quality becomes a shared engineering practice, not a final handover step.   What this role is not * This is not a traditional end-stage QA role where the main responsibility is to test finished features after development is complete. * This role is also not limited to clicking through UI flows or executing predefined test cases. * The role is part of the engineering process and focuses on early validation, data-quality verification, regression testing, benchmarking, and release confidence. IHR PROFIL We are looking for someone with a few years of hands-on experience in software quality, test engineering, data validation, or data platform testing. Someone who thinks like an engineer, not only as a tester. The ideal candidate has experience beyond manual testing and is comfortable working closely with engineering teams, reading technical designs, understanding data flows, and creating practical validation strategies. You should have: * 2+ years of experience in quality engineering, test engineering, data engineering testing, SDET, or a similar role. * Experience testing complex software systems, preferably involving data processing, backend services, APIs, pipelines, or platform workflows. * Strong ability to understand product requirements and convert them into concrete test scenarios. * Experience with test design, exploratory testing, regression testing, and acceptance criteria validation. * Practical experience with Python-based testing or scripting. * Experience working with datasets, structured data, logs, outputs, or data-quality validation. * Familiarity with CI/CD workflows and modern development processes. * Ability to communicate findings clearly to developers, product managers, and technical leads. * A structured mindset and the ability to define repeatable quality processes from scratch. Technical skills Relevant experience may include: * Python and pytest or similar testing frameworks * REST API testing * Basic understanding of CI/CD pipelines * Basic understanding of docker or containerized environments * Observability tools, logs, metrics, or dashboards The following are bonus: * Basic understanding of Data/ML Pipelines * Performance benchmarking * time-series data processing

HUMAN DATA QUALITY ENGINEER (FOUNDING TEAM)    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 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.   What You’ll Be Doing * 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. What You’ll Bring to the Role * 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. in * 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 yout 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. Even Better if you have: * 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. Why Prolific is a great place to work We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioral data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems. We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviors into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breath and the best of humanity. Working for us will place you at the forefront of AI innovation, providing access to our unique human data platform and opportunities for groundbreaking research. Join us to enjoy a competitive salary, benefits, and remote working within our impactful, mission-driven culture. At Prolific, our compensation packages for eligible roles include base salary, equity, and benefits. Many roles also include the opportunity to earn a cash variable element, such as a bonus or commission. Each job posting shows a salary range that reflects the minimum and maximum target for new hires, based on the role’s location as well as your skills, experience, and relevant education or training. Your recruiter will also be happy to share the specific salary range for your preferred location during the hiring process.   LINKS TO MORE INFORMATION Website  Youtube  Benefits  External Handbook    Privacy Statement By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal information.