Principal Applied Scientist, Secure Work Enablement

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset. 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. You will define the technical roadmap that transforms Advisor from a recommendation engine into a fully autonomous agent capable of reasoning across complex system states, orchestrating multi-step remediation workflows, and continuously learning from outcomes. This is a leadership role requiring someone who can set the scientific direction for agentic AI in the troubleshooting domain, drive breakthroughs in reasoning and planning under uncertainty, and build the ML foundations that make Advisor the most trusted AI companion in enterprise workspace management. You'll define and drive the scientific strategy for Advisor's agentic capabilities, establishing the research agenda that keeps us at the frontier of autonomous troubleshooting and self-healing systems. Architect agentic reasoning systems that enable Advisor to autonomously diagnose root causes across complex multi-signal environments — correlating performance telemetry, session behavior, network conditions, and infrastructure state to identify problems before users feel them. Design and build planning and orchestration frameworks that allow Advisor to compose multi-step remediation actions, reason about dependencies and risks, and execute recovery workflows with appropriate human-in-the-loop guardrails. Develop advanced causal inference models that move beyond correlation to true root-cause identification, enabling Advisor to distinguish symptoms from underlying issues across interconnected system layers. Build continuous learning systems where Advisor improves from every interaction — leveraging reinforcement learning from human feedback (RLHF), outcome-driven reward signals, and retrieval-augmented generation (RAG) to expand its troubleshooting knowledge over time. Pioneer natural language reasoning capabilities that allow Advisor to explain its diagnostic process, communicate findings clearly to administrators, and engage in collaborative problem-solving dialogue. Establish evaluation frameworks and safety mechanisms that ensure Advisor's autonomous actions maintain customer trust — defining confidence thresholds, escalation policies, and rollback strategies for automated remediation. Influence the broader organization's AI strategy by identifying opportunities to extend Advisor's agentic patterns to adjacent problem spaces and by publishing findings that advance the state of the art in autonomous IT operations. Key job 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, and 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, and 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. About the team Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity and AmazeCon conferences, inspire us to never stop embracing our uniqueness. We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Minimum 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 Preferred qualifications - 10+ years of relevant work in industry or academia experience - Knowledge of problem solving, algorithm design and complexity analysis - Experience creating novel algorithms and advancing the state of the art - Have peer-reviewed scientific contributions in premier journals and conferences 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 - 198,900.00 - 269,000.00 USD annually