AI Research Engineer

AI Research Engineer

Prolific

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.

The Role

Prolific AI Research (PAIR) is the AI research group at Prolific ( https://labs.prolific.com/ ). 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.

What You’ll Be Doing

Research & Innovation

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

Technical Implementation

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, distillation, and other ML engineering practices to support research goals

Cross-Functional Collaboration

Work closely with ML engineers, data scientists, and product teams to translate research insights into practical applications

Mentor team members on advanced AI concepts and emerging research directions