About this role
The Frontier AI Assets (FAI) team is looking for a passionate, talented, and inventive Principal Applied Scientist with a strong deep learning and reinforcement learning background to build the training and evaluation assets that advance frontier Generative AI (GenAI) with Large Language Models (LLMs). AI models become more capable largely because of the learning environments, the RL gyms, in which they are trained. FAI builds those gyms and the platform that certifies them, so that model training teams across Amazon can rely on high-quality, graded assets. As a Principal on this team, you will set the technical direction for how these gyms and the tooling around them are built, evaluated, and scaled.
Key job responsibilities
As a Principal Applied Scientist with FAI, you will:
- Set technical direction across data collection, sampling, and evaluation for RL gyms focused on agentic tool use across a range of real-world domains.
- Design and lead experiments that produce reliable, efficient signal on what drives model improvement.
- Build gym-building tooling and automated quality control that scale with demand.
- Advance the science of how gyms are evaluated and validated before they're used in model training.
- Act as a hands-on technical leader: produce exemplary, high-quality assets yourself, and audit and support more junior scientists and engineers.
- Partner closely with domain experts and cross-functional teams, turning real-world problems into challenging, high-value tasks.
- Lead scientific reviews, scrutinize experimental design and verification, probe assumptions, and align teams toward coherent strategies.
Contribute to hiring, mentorship, and the development of other scientists.
About the team
The FAI team has the mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers. We build frontier training and evaluation assets, RL gyms and the certification platform behind them, that directly shape how Amazon's models are trained and improved. We value scientific depth, hands-on building, and turning research into measurable impact at scale.