AmazonPosted 3mo ago

Principal Applied Scientist, AWS Agentic AI

Principal Applied Scientist, AWS Agentic AI at Amazon scores 94 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, NY, New Yorkleadfull-time$229k-$309k

AI in this role

ragfine-tuningcomputer-visionnlpai-safety

Amazon Web Services (AWS) is looking for a Principal Applied Scientist to join the Quick Science team. Quick is AWS’s enterprise generative AI assistant that helps users answer questions, summarize documents, generate content, take actions, and automate workflows using information across enterprise systems. As a key member of this team, you will lead research and development efforts in generative AI and Agentic AI to enable intelligent agents that perform complex reasoning, automate multi-step workflows, and make enterprise users significantly more productive.

Key job responsibilities
You’ll work on building and optimizing multi-modal foundation models, training and fine-tuning state-of-the-art LLMs, and architecting systems that scale efficiently across domains. This role blends science leadership, hands-on innovation, and deep collaboration with engineering teams to bring research into production.

Basic qualifications

- PhD in Machine Learning, Computer Science, Electrical Engineering, or a related technical field OR a Master’s degree with 5+ years of relevant industry or research experience.
- Industry experience developing machine learning models for real-world applications.
- Experience with generative AI, including model training or building systems with pre-trained foundation models.
- Proven record of peer-reviewed publications or granted patents in AI/ML.
- Proficiency in Python or similar programming languages.
- Experience in at least one of the following areas: natural language processing (NLP), large language models (LLMs), computer vision, or Agentic AI.

Preferred qualifications

- Experience applying generative AI to enterprise or multi-modal tasks (e.g., code generation, document understanding, or task planning).
- Strong understanding of agentic architectures, autonomous systems, or task orchestration.
- Hands-on experience with scalable ML infrastructure, distributed training, or optimization of large models.
- Deep knowledge of AI safety, hallucination mitigation, or retrieval-augmented generation (RAG).
- Experience mentoring junior scientists and influencing cross-functional stakeholders.
- Ability to think strategically and communicate complex technical topics to non-experts, including senior leadership.
- Track record of shipping scientific innovations into customer-facing products at scale.

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

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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, CA, Santa Clara - 228,700.00 - 309,400.00 USD annually
USA, NY, New York - 218,800.00 - 295,900.00 USD annually
USA, WA, Seattle - 198,900.00 - 269,000.00 USD annually

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RagFine TuningComputer VisionNlpAI Safety

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
How do you think about the risk of an AI system in this kind of role failing silently?

Adapt your resume

List these exact terms on your resume: Rag, Fine Tuning, Computer Vision, Nlp, and AI Safety. 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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