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.
BASIC QUALIFICATIONS
- Experience programming in Java, C++, Python or related language
- PhD in Computer Science, Electrical Engineering, Mathematics, or a related technical field; or Master's degree with significant additional applied research experience.
- Strong hands-on experience building machine learning models and systems for real-world applications, with deep learning and neural methods.
- Experience with reinforcement learning and/or building RL gyms, learning environments, or agentic tool-use systems.
- Experience setting technical direction across data, modeling, and evaluation, and dealing with ambiguity and competing objectives.
- Experience leading experienced scientists/engineers and developing more junior members.
PREFERRED QUALIFICATIONS
- 10+ years of relevant work in industry or academia experience
- Experience building automated evaluation, quality-control, or verification tooling for training data or environments.
- Experience with agentic tool use and improving the capabilities of LLMs (for example multi-step, tool-using agents).
- Track record of creating novel algorithms and advancing the state of the art; peer-reviewed contributions in premier journals and conferences.
- Experience with large-scale distributed training/compute and modern ML frameworks.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, Sunnyvale - 228,700.00 - 309,400.00 USD annually
USA, WA, Bellevue - 198,900.00 - 269,000.00 USD annually
