Principal Applied AI Scientist: Knowledge & Temporal Systems

Amazon Inc.; Miami, Northern (FL, KY); Hybrid; USD 199,000 - 269,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Parental leave

Job summary

Amazon Inc. is seeking a Principal Applied Scientist to own the scientific direction of AI work at our U.S. Development Center. You will lead a team of applied scientists and MLEs, shaping long‑term strategy while prototyping ideas on real data to prove value.

You will own strategies for knowledge representation, retrieval, and temporal reasoning, and build robust evaluation for complex agentic systems. You will mentor the team and drive production‑readiness across the stack.

Qualifications

• PhD or Master’s with 8+ years of applied science experience.
• 10+ years of experience shipping ML/AI systems to production.
• Deep expertise in large language models and at least two of: information retrieval, knowledge representation/graphs, reinforcement learning, agentic system design, or evaluation methodology for generative systems.
• Hands‑on proficiency in Python and the ability to prototype independently in a production codebase.
• Track record of publications, patents, or equivalent evidence of original scientific contribution

Responsibilities

• Own the scientific strategy for how organizational knowledge is represented, kept current, and retrieved: extraction, entity resolution, deduplication, graph structure, and retrieval that unifies graph, semantic, keyword, and temporal search.
• Advance temporal reasoning. Knowledge changes: facts are revised, decisions are reversed, priorities move. Representing what superseded what and when, and preserving the provenance to distinguish confirmed information from inferred information, is among the hardest open problems in this space.
• Define the science of proactive behavior. When is it right for an AI system to interrupt a human? These are precision‑critical problems where a false positive costs far more than a miss, and where the right threshold varies by team and by individual.
• Lead our measurement science. Build evaluation for completeness and correctness across a multi‑component agentic system, converging on a small number of trustworthy primary metrics rather than a sprawl of component scores. Judge honestly when an offline gain is real and when it is an artifact of a sparse dataset.
• Build the data that doesn't exist. The most valuable phenomena in this domain are also the rarest, which makes naturally occurring examples too scarce to learn from. Design synthetic and simulated data pipelines that generate controlled, realistic scenarios so these capabilities can be developed and tested at all.
• Own the learning loop. Turn human interaction into usable training signal, and set the direction for how the system improves from explicit feedback in the near term and from passive observation over the longer term.
• Make the efficiency calls. Decide where frontier models are required and where a smaller domain‑tuned model is sufficient, and build the cost and capacity measurement that makes it a data‑driven decision rather than an opinion.
• Raise the bar across the team. Mentor scientists, review designs, publish where the work merits it, and represent the science externally to customers and to the research community.

Skills

Python

Mentoring senior scientists

Publications/patents

Large language models

Information retrieval

Knowledge graphs

Agentic system design

Prototyping in production codebase

Education

PhD in Computer Science / ML / Statistics / related field

Master's degree + 8+ years applied science

Job description

Amazon Inc. is seeking a Principal Applied Scientist to own the scientific direction of AI work at our U.S. Development Center. You will lead a team of applied scientists and MLEs, shaping long‑term strategy while prototyping ideas on real data to prove value.

You will own strategies for knowledge representation, retrieval, and temporal reasoning, and build robust evaluation for complex agentic systems. You will mentor the team and drive production‑readiness across the stack.