[Hiring] 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

This role is 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. 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 opportunities
• Impactful, mission-driven culture