Job ID: 2826703 | Amazon Development Centre (London) Limited - C26
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems.
Key job responsibilities
As an Applied Scientist on our team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in multi-modal AGI models, with a focus on speech generation. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to build Machine Learning models for their application in speech generation.
This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations.
About the team
The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
BASIC QUALIFICATIONS
1. PhD, or a Master's degree and experience in CS, CE, ML or related field
2. Experience in patents or publications at top-tier peer-reviewed conferences or journals
3. Experience programming in Java, C++, Python or related language
4. Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
5. Experience in building machine learning models for business application
PREFERRED QUALIFICATIONS
1. Experience using Unix/Linux
2. Experience in professional software development
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Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Posted: September 24, 2024
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