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AI Research Engineer
ICT
💰 Salary Not shared
📊 Market 4,500 GBP–7,500 GBP/mo (54,000 GBP–90,000 GBP/yr)
About the Company
Prolific is not just another player in the AI space - we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human generated data that truly differentiates products and models.
About the Role
Prolific AI Research (PAIR) is the AI research group at Prolific. We work on the science of evaluation: how to measure AI systems well, grounded in real human judgement at population scale. We are seeking a Research Engineer to conduct innovative research in key AI areas including evaluation methodologies (particularly those grounded in human data), alignment techniques, and synthetic data generation. You'll bridge the gap between cutting edge AI research and practical applications, helping to translate research insights into valuable products and services. As part of our commitment to contribute to the wider AI community, you'll be involved in writing papers that advance the field. Your work will directly impact how we understand, evaluate, and improve AI systems through high quality human data.
Responsibilities
- ● Lead independent research projects in AI evaluation methodologies, alignment techniques, and synthetic data generation.
- ● Design and implement novel evaluation frameworks for LLMs and agent systems that are grounded in human data.
- ● Contribute to the academic AI community through publications and open source contributions.
- ● Stay at the forefront of AI research and pioneer innovative approaches to tackle pressing open challenges in the field.
- ● Design and conduct rigorous experiments to study AI models and systems with sound methodological approaches.
- ● Develop scalable frameworks for systematic evaluation of model behaviours and capabilities.
- ● Create tools and frameworks that transform research insights into practical applications.
- ● Build infrastructure to support large scale research experiments when needed.
- ● Apply knowledge of model fine tuning, optimization techniques, and distillation.
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