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Principal Applied Scientist, Secure Work Enablement

Amazon.com Inc

  • Seattle, WA
  • 24 days ago
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    Skills

    • Amazon Web Services (AWS)unmatched
    • Artificial Intelligence (AI)unmatched
    • Best Practicesunmatched
    • Communication Skillsunmatched
    • Develop and Maintain Customersunmatched
    • Identify Issuesunmatched
    • Leadershipunmatched
    • Mentoringunmatched
    • Patentsunmatched
    • Problem Solving Skillsunmatched
    • Product Engineeringunmatched
    • Product Strategyunmatched
    • Production Systemsunmatched
    • Reinforcement Learningunmatched
    • Root Cause Analysisunmatched
    • Scientific Publicationsunmatched
    • System Architectureunmatched
    • Team Playerunmatched
    • Technical Supportunmatched
    • Telemetryunmatched
    • Workplace Issuesunmatched

    Description

    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.

    Numbers & Facts

    LocationSeattle, WA
    IndustryRetail
    Company Size10,000 employees or more
    Year Founded1994
    Websitehttp://Amazon.com/militaryroles

    About Company

    At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

    Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

    It’s Always Day 1
    At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

    Our Leadership Principles
    Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles

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