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Applied Scientist, AGI Information, Cambridge
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Client:
Evi Technologies Limited
Location:
Cambridge, United Kingdom
Job Category:
Other
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EU work permit required:
Yes
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Job Reference:
8beb42000e3e
Job Views:
69
Posted:
12.08.2025
Expiry Date:
26.09.2025
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Job Description:
We are looking for a researcher in cutting-edge LLM technologies for applications across Alexa, AWS, and other Amazon businesses. In this role, you will innovate in the fastest-moving fields of current AI research, in particular in how to integrate a broad range of structured and unstructured multimodal information into AI systems (e.g. with RAG techniques), and get to immediately apply your results in highly visible Amazon products.
If you are deeply familiar with LLMs, natural language processing and machine learning, this may be the right opportunity for you. Our fast-paced environment requires a high degree of independence in making decisions and driving ambitious research agendas all the way to production. You will work with other science and engineering teams as well as business stakeholders to maximize velocity and impact of your team's contributions.
It's an exciting time to be a leader in AI research. In Amazon's AGI Information team, you can make your mark by improving information-driven experience of Amazon customers worldwide!
BASIC QUALIFICATIONS
- PhD, or a Master's degree and experience in CS, CE, ML or related field
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in building machine learning models for business application
- Experience with natural language processing and/or processing of multi-modal data (e.g. images)
PREFERRED QUALIFICATIONS
- Experience using Unix/Linux
- Experience in professional software development
- Experience with training and evaluating LLMs
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