Applied Scientist III, Foundation AI Assets (FAI)

The Foundational AI Assets (FAI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs).

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.

• Experience with neural deep learning methods and machine learning
• 5+ years of building machine learning models for business application experience
• 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

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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, MA, Boston – 167,100.00 - 226,100.00 USD annually

• Design and implement novel machine learning and deep learning models to address scientifically complex problems, taking solutions from research through production deployment while maintaining high standards for code quality and system scalability.
• Drive the scientific agenda for your team by identifying new opportunities, proposing research initiatives, and securing alignment with technical leadership on strategic priorities.
• Evaluate and improve model performance using rigorous experimental methodologies, applying techniques such as feature engineering, hyperparameter optimization, and advanced neural network architectures (e.g., Transformers, CNNs) built with frameworks like PyTorch or TensorFlow.
• Mentor and coach scientists and engineers across the organization, conduct thorough code and design reviews, and contribute to the professional growth of your peers while raising the technical bar for the team.
• Collaborate cross-functionally with engineering, product, and science partners to translate ambiguous business problems into well-defined scientific approaches, balancing short-term customer needs with long-term technical vision.

A day in the life

You might start your morning reviewing experiment results and refining a model architecture before joining a design review where you provide guidance on a colleague's approach to a new problem. After lunch, you dive into writing production-quality code for a critical component of your system, then shift to drafting a scientific document that outlines your team's next research direction. Throughout, you engage with engineers and scientists across teams, sharing your expertise and learning from theirs.