> Markdown version of [/jobs/ext/4614822-sr-applied-scientist-agi](https://www.wearedevelopers.com/jobs/ext/4614822-sr-applied-scientist-agi). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Applied Scientist, AGI - **Company:** Amazon.com, Inc. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, Business Software, C++ (Programming Language), Profiling, Software Debugging, Python (Programming Language), Machine Learning, Large Language Models, Deep Learning, Generative AI, Machine Learning Operations - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25303_f9c39f463f0380d0b3f7835ac0241561 ## About the Role * 3+ years of building machine learning models for business application experience * Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability * PhD, or Master's degree and 6+ years of applied research experience * Experience programming in Java, C++, Python or related language, * Experience with building agents OR training LLMs for agentic tasks ## Description The Foundational AI (FAI) 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)., As an Applied Scientist with the FAI team, you will support the development of RL Gyms, assess their usefulness for the frontier model advancement and build techniques to advance the state of the art with LLMs. You will support the foundational model development in an applied research role. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs.