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Prolific

Building a better world with better data.

Lead Applied Scientist

Research ScientistResearch ScientistContractRemoteLeadTeam 51-200Since 2014H1B SponsorCompany SiteLinkedIn

Location

United Kingdom

Posted

9 days ago

Salary

0

Seniority

Lead

Job Description

Lead Applied Scientist

Prolific

Role Description As an Applied Scientist, you will design and prototype AI/ML methods that improve data quality, scale human judgement, and support robust AI evaluation workflows. You will work on applied problems such as: - Quality modelling - Judgement aggregation - Evaluation design - LLM-assisted review - Reliability testing for AI systems Ideal for someone with deep scientific judgement, strong applied ML skills, and a practical bias toward methods that work in real customer and product contexts. This is not a pure research role or a production ML engineering role. You will turn ambiguous problems into clear methodologies, benchmarks, models, and prototypes that product and engineering teams can adopt. What You'll Be Doing: - Prototype AI/ML methods to improve human data quality, judgement aggregation, and AI evaluation workflows. - Design experiments, benchmarks, and reliability tests to measure whether new methods improve quality, efficiency, or customer outcomes. - Apply classical ML, statistics, LLMs, and agentic techniques where they create practical value. - Use modern AI tools to accelerate prototyping, experimentation, and iteration. - Partner closely with product and engineering to translate scientific methods into scalable platform capabilities. - Communicate technical assumptions, trade-offs, and recommendations clearly across technical and non-technical teams. Qualifications - PhD or MSc in Computer Science, Mathematics, Statistics, Machine Learning, or a related field. - 3+ years of applied ML, AI research, or data science experience with demonstrated real-world impact. - Experience with human-in-the-loop AI systems, including RLHF, annotation pipelines, data quality modelling, judgement aggregation, benchmarks, or AI evaluation. - Fluency with modern LLM and agentic techniques, such as Retrieval-Augmented Generation (RAG), LLM-as-judge, multi-agent workflows, synthetic data generation, and automated quality review. - Strong Python skills and the ability to quickly build, test, and iterate on working prototypes. - Good judgement on when to use simple statistical methods, classical ML, LLMs, or agentic approaches. - Ability to translate ambiguous product or customer problems into clear hypotheses, experiments, metrics, and reusable methodologies. - Strong cross-functional communication and experience partnering with product and engineering teams. Benefits - Competitive salary - Remote working - Impactful, mission-driven culture

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