Principal Applied Scientist, AAIS

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box making complex things easy and hard things possible with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction and we are looking for builders who share that mindset.

AI assistants are getting genuinely good at remembering individuals: your preferences your projects the thread you left open last week. But that memory stops at the edge of one persons usage. It doesnt reach the level at which real work happens where the knowledge that matters is spread across many people where one persons decision changes what everyone else should do next and where nobody has the full picture. Were building AI that operates at that level: a durable accurate understanding of how a team works used to make that team measurably faster.

We are looking for a Principal Applied Scientist to own the scientific direction of that work. This is a broad ambiguous high-leverage charter. The problems span knowledge representation temporal reasoning retrieval agentic behavior and the measurement science needed to know whether any of it is working. You will not be handed a well-posed problem. You will decide which problems are worth posing.

This is a science leadership role not a solo research role. You will set direction and raise the scientific bar across a team of applied scientists and MLEs while staying deep enough in the work to prototype an idea yourself and prove it on real data.

Key job 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 Representing what superseded what and when and preserving the provenance to distinguish confirmed information from inferred information is among the hardest open problems in this space.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 doesnt 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.

A day in the life
You might spend the morning in a design review arguing that a proposed approach wont survive contact with real data the afternoon writing a prototype yourself to demonstrate the alternative and the end of the day convincing an engineer that the capability is worth a sprint. Our sequencing is deliberate: try the idea on intuition validate it on real data by inspection then measure it then operationalize it. Scientists here are expected to identify a problem justify it recruit others to it and drive it into production across whatever parts of the system that requires. Ownership follows the problem not the org chart.

About the team
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description we encourage candidates to apply. If your career is just starting hasnt followed a traditional path or includes alternative experiences dont let it stop you from applying.

Amazon Web Services (AWS) is the worlds most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating thats why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home theres nothing we cant achieve in the cloud.

Here at AWS its in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences including our Conversations on Race and Ethnicity and AmazeCon conferences inspire us to never stop embracing our uniqueness.

Were continuously raising our performance bar as we strive to become Earths Best Employer. Thats why youll find endless knowledge-sharing mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

- PhD in Computer Science Machine Learning Statistics or a related quantitative field; or a Masters degree with 8 years of applied science experience
- 10 years of experience building and shipping machine learning or AI systems that reached production users
- Deep expertise in large language models and at least two of: information retrieval knowledge representation and graphs reinforcement learning agentic system design or evaluation methodology for generative systems
- Demonstrated experience setting technical and scientific direction for a team of scientists including mentoring senior scientists
- 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

- Experience with agentic and multi-turn systems including RL-based post-training environment simulation or agent harness evaluation
- Experience designing evaluation frameworks for open-ended or subjective tasks where ground truth is expensive or unavailable including synthetic data generation
- Experience with memory personalization or long-horizon context systems for LLM applications
- Experience with temporal knowledge representation entity resolution or knowledge graph construction at scale
- Experience taking a product from prototype to launch under ambiguity including making the judgment call on when quality is sufficient to ship
- Experience with model distillation or domain-specific tuning to reduce inference cost
- Scientific breadth across multiple ML domains and comfort operating outside your original specialization

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

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 WA Seattle - 198900.00 - 269000.00 USD annually