Darcie Talent
Research Engineer
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An exciting opportunity has arisen for an experienced Research Engineer to join a world-class team working at the forefront of AI research and engineering.
The successful candidate will work alongside leading researchers and engineers, developing novel approaches to challenging AI problems and translating cutting-edge research into impactful real-world applications.
This is an excellent opportunity for someone who wants to work at the intersection of research and engineering, with the opportunity to contribute to publications at leading AI conferences and collaborate with prominent academic and industry partners.
The Role
- Develop and implement novel machine learning algorithms and techniques
- Work closely with world-class researchers and engineers on challenging AI problems
- Explore new approaches across areas including reinforcement learning, large language models and optimisation
- Translate research concepts into practical, scalable implementations
- Contribute to research suitable for publication at leading AI conferences
- Collaborate with leading academic and industry partners
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Experience Required
The ideal candidate will have:
- Deep expertise in at least one of the following areas:
- Reinforcement Learning (RL)
- Large Language Models (LLMs)
- Optimisation for Deep Learning
- Strong experience implementing machine learning algorithms and techniques
- A strong interest in AI research and emerging technologies
- The ability to work collaboratively within a highly talented research and engineering environment
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Why Consider This Opportunity?
- Work on cutting-edge AI challenges with a talented, multidisciplinary team
- Collaborate with leading academic and industry partners
- Access high-performance computing resources
- Work on novel AI research with the potential for significant real-world impact
- Publish research at leading conferences including NeurIPS, ICML and ICLR
- Work alongside some of the leading researchers and engineers in the field
Location
The position can be based in either Cambridge or London, near King’s Cross.
The organisation currently operates a hybrid working model, with 4 days per week in the office and 1 day working from home.
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