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Applied Scientist III, AWS Account Management and Governance

Job in Seattle, King County, Washington, 98127, USA
Listing for: Amazon Inc.
Full Time position
Listed on 2026-10-19
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), AI Business & Operations
Salary/Wage Range or Industry Benchmark: 192000 - 260000 USD Yearly USD 192000.00 260000.00 YEAR
Job Description & How to Apply Below
Applied Scientist III, AWS Account Management and Governance

Job :  | Amazon Development Center U.S., Inc.

We are seeking an Applied Scientist III to own the technical vision and development of an AI-based governance intelligence platform that will redefine how account and organizational governance is defined, validated, and enforced s mission-critical platform will leverage AI-driven autonomous agents to conduct proactive, intelligent governance operations across the account lifecycle.

The platform will integrate deeply with organizational management, identity, and cloud-native control systems to provide self-serve, automated governance insights, policy verification, and enforcement mechanisms. The Governance Foundations team is building a unified governance foundation that consolidates account management controls — including automated enrollment, managed policies, and account lifecycle guardrails — into a single, intelligent, scalable system.

As a senior science leader on the team, you will operate with a high degree of autonomy on ambiguous, open-ended problems that span multiple organizations. You will set the multi-year scientific direction, make the architectural and modeling decisions that others build on, and drive alignment across applied science, engineering, product, and partner teams — up to and including senior leadership. This is an opportunity to shape the future of AI-driven governance assurance at enterprise scale: defining standards, influencing company-wide account governance posture, and leading technical innovation at the highest level.

Key

job responsibilities
  • Own and define the next-generation governance intelligence and policy-matching platform, setting the multi-year technical strategy for AI-driven governance automation across applied science and engineering teams and resolving the hardest, most ambiguous problems in the space.
  • Build on and advance a multi-agent LLM framework for autonomous governance operations, and influence partner organizations to adopt the most promising approaches — including human-in-the-loop enforcement boundaries that cannot be bypassed by autonomous agents.
  • Develop a highly scalable intelligent governance agent framework — combining traditional ML and LLM-based techniques — that enables internal teams to automate validation, classification, and enforcement across structured and unstructured policy and account data.
  • Provide technical leadership and scientific mentorship across the broader organization — raising the bar on science quality, growing other scientists and engineers, and representing the team's technical direction to senior leadership — while still diving deep hands‑on to diagnose complex system performance and correctness problems in distributed governance control planes.
  • Define the scientific metrics, evaluation methodology, and quality bar by which the platform's governance decisions are measured and trusted.
About the team

The Accounts Management and Governance team builds the unified governance foundation for account and organizational management  team consolidates account lifecycle controls, policy propagation, and enforcement guardrails into a single intelligent system, ensuring the experiences Amazon delivers to customers uphold our highest standards of security, correctness, and operational integrity. The science team harnesses AI to strengthen Amazon's governance posture more efficiently and effectively.

Basic

Qualifications
  • PhD, or Master's degree and 6+ years of applied research experience
  • Experience programming in Java, C++, Python or related language
Preferred Qualifications
  • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  • Experience with large scale distributed…
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