Senior Applied Scientist, Artificial General Intelligence

DESCRIPTION The Amazon Artificial General Intelligence (AGI) team is seeking a talented, self-driven Senior Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI.
Key job responsibilities
- Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI.
- Collaborate with cross-functional teams to architect and execute technically rigorous AI projects.
- Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines.
- Engage in effective technical communication (written & spoken) with coordination across teams.
- Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility.
- Publish research papers in internal and external venues of repute
- Support on-call activities for critical issues
A day in the life
This Senior Applied Scientist will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions in LLMs. They will also collaborate with partner teams across roles (product/program managers, data annotators, developers, etc) to deliver robust models in production.
They will mentor other scientists, review and guide their work, and help develop roadmaps for the team. They will work closely with partner teams to deliver platform features that require cross-team leadership.
We are open to hiring candidates to work out of one of the following locations:
Bellevue, WA, USA BASIC QUALIFICATIONS - 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 3+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Proficiency in coding and software development, with a strong focus on machine learning frameworks.
- Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
- Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting.