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. 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.

About the team

AWS is on a mission to transform how businesses operate by delivering intelligent, cloud-powered applications. Our Applied AI Solutions organization accelerates customer success through intuitive, differentiated technology that solves enduring business challenges - blending vision with real-world expertise to build turnkey solutions that are easy to adopt and built to scale.

Within this organization, we are building the next generation of secure, intelligent workspaces - environments purpose-built for human-AI collaboration at enterprise scale. Our WorkSpaces Advisor is an AI-powered troubleshooting companion that proactively detects, diagnoses, and resolves workspace issues, transforming reactive IT support into intelligent, autonomous problem-solving.

BASIC 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