Lead Applied Scientist (12 Month Fixed Term Contract)

Requirements

• 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.
• 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.

What the job involves

• 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, and reliability testing for AI systems.
• 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.
• 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.