Applied Scientist, Agentic AI, AWS Governance

Amazon is hiring an Applied Scientist, Agentic AI, AWS Governance in Seattle, United States. It pays $143k-$193k 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 posting mentions LangGraph, AI agents and fine tuning among its tools and skills.
Level classes it as a mid-level role.
Amazon has 21,000+ other open roles on Level.
Posted in the last few days.

AI in this role

langgraph

ai-agentsfine-tuningcomputer-visionnlp

Do you want to build the ML models that power every compliance verdict customers and auditors rely on? We are building a new Governance, Risk, and Compliance (GRC) service at AWS where every action — human or agent — is recorded, assessed, and provably governed, continuously and at any scale.

As an Applied Scientist on this team, you will own the Natural Language Inference (NLI) model at the center of our compliance engine: fine-tuning, calibrating, and monitoring it in production. You will also design the science behind framework decomposition, evidence auto-linking, and a new agent-context system that tackles retrieval, grounding, and hallucination measurement for AI agents. Your work will directly shape the accuracy and trustworthiness of every customer-visible result we produce.

Key job responsibilities
- Own the production NLI model end-to-end: fine-tune, promote, calibrate thresholds, and monitor for drift to ensure accurate compliance verdicts across frameworks.
- Design and implement framework decomposition pipelines that translate compliance controls into NLI hypotheses, including enrichment and per-framework threshold calibration.
- Build and improve the evidence auto-linking system using ranking, embedding, and reranking techniques, tuning precision-recall tradeoffs and addressing OCR quality challenges.
- Drive the quality flywheel by establishing labeling standards, designing offline and online evaluation methods, orchestrating retraining cycles, and ensuring privacy-safe data handling.
- Research and develop the agent-context capability — retrieval and representation of lifecycle signals, cross-framework knowledge, memory, grounding, and hallucination measurement — collaborating with engineering pods to bring these models into production.

A day in the life
You will split your time between collaborative and heads-down work. On the collaborative side, you will partner with several pods of software development engineers in an Agile cadence — aligning on data collection needs, sharing experiment results, and integrating models into production services. On the heads-down side, you will dig into model training runs, analyze evaluation metrics, and iterate on threshold tuning until the numbers meet the bar. Your work is roadmap-driven, giving you the space to plan experiments methodically while still responding to new framework onboarding requests as they arise.

About the team
Our team builds the AI and ML systems behind AWS Governance, Risk, & Compliance service, where science is not auxiliary — it is the core. Every customer-visible compliance result flows through models our scientists create and maintain. We are investing in next-generation capabilities including observability and governance for AI agents, and the multi-quarter Agent Context initiative is an organization-defining program that will need technical leaders.

Growth here is tangible. You will own the full model lifecycle from training through production monitoring, develop rare expertise at the intersection of ML and compliance, and have a clear path to expand your scope into technical leadership as the service scales.

Basic qualifications

- 2+ years of building models for business application experience
- 2+ years of programming in Java, C++, Python or related language experience
- 2+ years of hands-on predictive modeling and large data analysis experience

Preferred qualifications

- PhD in computer science, machine learning, engineering, or related fields
- Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders
- Experience with one of the following areas: machine learning technologies, Reinforcement Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP) or related applications
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Extensive experience applying theoretical models in an applied environment.
- Experience with building agentic systems using LLMs and familiar with common agentic framework such as Strands Agent, LangGraph, etc.
- Solid knowledge on LLM techniques and experience with LLM post-training.

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, VA, Arlington - 142,800.00 - 193,200.00 USD annually
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually

How we rate this

Applied Scientist, Agentic AI, AWS Governance at Amazon rates 97 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

Applied Scientist, Agentic AI, AWS Governance is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $143k-$193k a year.
The role is based in US, WA, Seattle.
The job was posted on October 26, 2026.
Seniority: mid.
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

AI agentsFine-tuningComputer visionNLPLangGraph

Also common in applied scientist jobs at this level

Generative AI TensorFlow Spark scikit-learn PyTorch RAG

Pay

This job starts at: $143k
Typical applied scientist job: $172k

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

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
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?
Walk me through how you've used LangGraph in your day-to-day work.

Adapt your resume

List these exact terms on your resume: AI agents, Fine-tuning, Computer vision, NLP, and LangGraph. 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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