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Key job responsibilities
- Work with audit teams, product managers, engineers, and more senior scientists to deliver machine learning and generative AI products that carry real degrees of ambiguity, scale, and complexity.
- Design, build, and evaluate agentic AI systems — multi-agent workflows, retrieval-augmented generation, and tool-using agents — that automate and augment audit work, applying rigorous LLM-as-judge and human-aligned evaluation to measure and improve output quality.
- Apply statistical analysis and classical machine learning using SQL and scripting languages like Python/R over large datasets to develop insights and recommendations that strengthen internal audit.
- Architect secure, scalable solutions on AWS machine learning and generative AI services (e.g., Bedrock, AgentCore, SageMaker), owning the full lifecycle from prototype through production deployment, monitoring, and iterative improvement, working closely with auditors to understand their business needs.
- Build and maintain the team's production and experimentation infrastructure, including deployment pipelines, observability and tracing, and evaluation harnesses.
- Advance applied research by exploring emerging techniques and sharing findings through internal and external publications, talks, and conferences.
A day in the life
As an Applied Scientist, you will help shape and execute a product roadmap that connects risk to the business, building AI products — increasingly centered on large language models and agentic systems — that make audit work more effective and efficient. Your work spans the full arc of applied science: framing ambiguous problems, prototyping with the latest generative AI techniques, building rigorous evaluations, and deploying solutions in production. The ideal candidate pairs a strong foundation in data science and machine learning with a builder's instinct for production architecture, thrives on ambiguity, and stays close to a fast-moving research frontier.
About the team
Internal Audit’s mission is to help our businesses improve controllership, operational efficiency, and customer experience. Basic Qualifications: - Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- Experience researching about machine learning, deep learning, NLP, computer vision, data science
- Experience building applications with large language models (e.g., prompt engineering, retrieval-augmented generation, or agentic/tool-using workflows). Preferred Qualifications: - Experience implementing algorithms using both toolkits and self-developed code
- PhD in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
- Experience in solving business problems through machine learning, data mining and statistical algorithms
- Experience implementing a cloud-based technology solution, or experience architecting/operating solutions built on AWS
- Experience designing and running evaluation frameworks for generative AI (e.g., LLM-as-judge, human-alignment measurement, benchmarking output quality).
- A track record of applied-research output: publications, conference talks, or patents.
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 - 136,000.00 - 184,000.00 USD annually
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