Principal Applied Scientist, Secure Work Enablement

About the role

We are looking for a Principal Applied Scientist to own and advance the scientific vision for WorkSpaces Advisor — our agentic AI system that serves as an always-on troubleshooting companion for workspace administrators and end-users.

Responsibilities

• Set the scientific vision and long-term research agenda: Define what "best-in-class agentic troubleshooting" looks like scientifically, identify the key unsolved problems, and chart a multi-year path to solving them — securing buy-in from VP-level leadership.
• Deliver breakthrough solutions on highly ambiguous problems: Independently identify, frame, and solve novel research challenges in agentic AI for troubleshooting — problems where neither the approach nor the success criteria are pre-defined.
• Influence and align across the organization: Drive scientific alignment across product, engineering, and business teams. Translate complex ML concepts into actionable product strategy. Represent the science team in leadership forums and planning cycles.
• Build and elevate scientific excellence: Mentor scientists and engineers across the team. Establish best practices for experimentation, evaluation, and deployment of agentic systems. Set the standard for scientific rigor and code quality.
• Deliver end-to-end production systems with outsized business impact: Own the full lifecycle from research to deployment for Advisor's core intelligence — making pragmatic trade-offs between long-term invention and near-term delivery while ensuring measurable customer and business outcomes.
• Advance the state of the art: Contribute to the external scientific community through publications, patents, and engagement that positions AWS as a leader in autonomous AI operations — bringing outside-in innovation back into Advisor.

Qualifications

• 5+ years of hands-on work in predictive modeling and analysis experience
• PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field
• Experience working in predictive modeling and analysis
• Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
• Experience programming in Java, C++, Python or related language
• Experience with leading experienced scientists as well as having a record of developing junior members from academia or industry to a career track in a business environment