Amazon

Applied Scientist, Artificial General Intelligence

Posted On

April 24, 2025

Location

Hyderabad, Telangana

Experience Level

Freshers/Experienced

Job Type

Research Science

Apply By

June 24, 2025

Job Description

The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in deep learning, to build industry-leading technology with Large Language Models (LLMs) and Multimodal systems.

Key job responsibilities
As part of the AGI team, the Applied Scientist will collaborate closely with talented colleagues to lead the development of advanced approaches and modeling techniques, driving forward the frontier of LLM technology. This includes innovating model-in-the-loop and human-in-the-loop approaches to ensure the collection of high-quality data, safeguarding data privacy and security for LLM training, and more. The Applied Scientist will also have a direct impact on enhancing customer experiences through state-of-the-art products and services.

A day in the life
An Applied Scientist with the AGI team will support the science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into practice.

The ideal candidate should be passionate about delivering experiences that delight customers and creating robust solutions. They will also create reliable, scalable and high-performance products that require exceptional technical expertise, and a sound understanding of Machine Learning.
- PhD, or Master's degree and 4+ years of building machine learning models or developing algorithms for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with Large Language Models (LLMs) or multimodal systems

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