Applied Scientist, Internal Audit

Socket.dev; Arlington (VA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

RSUs

Comprehensive benefits

Job summary

Amazon Internal Audit is seeking an Applied Scientist to accelerate Data Science & Risk Intelligence initiatives. You will own ambiguous, high-impact ML problems, build agentic AI systems, and scale solutions on AWS. The role emphasizes production experience, risk-aware evaluation, and mentoring team members.

The team works to improve controllership and operational efficiency, leveraging LLMs and advanced analytics to surface insights and mitigate risk across audits.

Qualifications

• PhD or Master’s plus 4+ years in CS/CE/ML or related field.
• Experience with ML fundamentals, training and inference lifecycles.
• Experience in AWS or cloud-based solutions.

Responsibilities

• Define and deliver ML and generative AI products with end-to-end ownership.
• Design and own agentic AI systems and evaluation methods.
• Analyze data with SQL/Python/R to derive actionable insights.
• Architect secure, scalable AWS ML solutions from design to production.
• Mentor junior scientists and contribute to roadmap.

Skills

ML fundamentals

Mentoring

Problem solving

Education

PhD

Master's in CS/CE/ML

Tools

Java

C++

Python

Job description

Are you passionate about data, enjoy solving complex analytical problems, leveraging industry leading agentic AI technologies to derive insight at scale - all in a challenging, fast-paced environment? We are seeking an Applied Scientist to accelerate the growth of Amazon Internal Audit’s Data Science & Risk Intelligence initiatives. The team builds ML and AI solutions that expand self-service data utilization by audit teams, utilizing the right methods to derive deeper patterns, and surface insights to gain holistic perspectives while amplifying potential risk mitigation.

Key job responsibilities

• Partner with audit teams, product managers, engineers, and scientists to define and deliver machine learning and generative AI products that carry significant ambiguity, scale, and complexity, owning problems end-to-end, from framing through measurable impact.
• Design, build, and own agentic AI systems, including multi-agent workflows, retrieval-augmented generation, and tool-using agents, that automate and augment audit work, and set the standard for how the team evaluates them through rigorous LLM-as-judge and human-aligned evaluation.
• 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 design through production deployment, monitoring, and iterative improvement.
• Own and evolve the team's production and experimentation infrastructure, including deployment pipelines, observability and tracing, and evaluation harnesses.
• Drive applied research by identifying and pursuing emerging techniques, and disseminate findings through internal and external publications, talks, and journal clubs.
• Raise the technical bar across the team, including mentor junior scientists and engineers, review designs and code, and help shape the product and technical roadmap.

A day in the life

As an Applied Scientist, you will own ambiguous, high-impact problems and help shape the technical roadmap that connects risk to Amazon. You will drive AI products that make audit work more effective and efficient, increasingly centered on LLM and agentic systems. You set technical direction across the full arc of applied science. That means framing problems, making architecture decisions, defining how the team evaluates quality, and delivering solutions in production. The ideal candidate pairs deep machine learning expertise with a builder's instinct for production architecture. They thrive on ambiguity, mentor others, and follow 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

• 3+ years of building models for business application experience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience building complex software systems that have been successfully delivered to customers
• Experience architecting/operating solutions built on AWS, or experience implementing a cloud-based technology solution

Preferred Qualifications

• Experience using Unix/Linux
• Experience in professional software development
• Experience designing and running evaluation frameworks for generative AI (e.g., LLM-as-judge, human-alignment measurement, benchmarking output quality).
• Hands-on experience with agentic AI frameworks and generative AI services (e.g., Amazon Bedrock, SageMaker, LangGraph, Strands).
• Experience building and maintaining production ML/AI infrastructure — deployment pipelines, observability/tracing, and experimentation environments.
• 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, VA, Arlington - 142,800.00 - 193,200.00 USD annually