Principal Applied Scientist, AAIS

Amazon is hiring a Principal Applied Scientist, AAIS in Seattle, United States. It pays $199k-$269k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

Builds AI ●●●● is how Level rates this applied scientist role: the job is building AI systems.
The listed pay starts 17% above the typical applied scientist posting on Level.
Tools and skills the posting cites include Python, PyTorch and machine learning.
Level classifies it as an executive role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Lead the scientific strategy and development of advanced multi-user AI assistant technologies and memory systems.

PythonPyTorch

machine-learningapplied-scienceknowledge-representationtemporal-reasoningretrieval-augmented-generationagentic-workflows

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 person's usage. It doesn't reach the level at which real work happens, where the knowledge that matters is spread across many people, where one person's decision changes what everyone else should do next, and where nobody has the full picture. We're 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 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.

A day in the life
You might spend the morning in a design review arguing that a proposed approach won't 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, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's 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, there's nothing we can't achieve in the cloud.

Here at AWS, it's 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.

We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Basic qualifications

- PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field; or a Master's 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

Preferred qualifications

- 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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t 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 https://amazon.jobs/en/benefits.

USA, WA, Seattle - 198,900.00 - 269,000.00 USD annually

How we rate this

Principal Applied Scientist, AAIS at Amazon rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Principal Applied Scientist, AAIS is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $199k-$269k a year.
The role is based in US, WA, Seattle.
The job was posted on September 18, 2026.
Seniority: executive.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Machine learningApplied ScienceKnowledge RepresentationTemporal ReasoningRetrieval Augmented GenerationAgentic WorkflowsPythonPyTorch

Also common in applied scientist jobs at this level

C++Java Deep learning Generative AI NLP TensorFlow

Pay

This job starts at: $199k
Typical applied scientist job: $169k

This posting starts 17% above the typical applied scientist job. See applied scientist jobs

Questions you could be asked

Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where applied science was part of your work. What did you do?
Tell me about a project where knowledge representation was part of your work. What did you do?
Tell me about a project where temporal reasoning was part of your work. What did you do?
Tell me about a project where retrieval augmented generation was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Machine learning, Applied Science, Knowledge Representation, Temporal Reasoning, and Retrieval Augmented Generation. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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