Responsibilities
- 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.
Requirements
- 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
- Benefits
- Remote working
- Impactful, mission-driven culture
Work Arrangement
Remote (Worldwide)
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