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Sr. Product Manager- Windows Engineering Systems

Define and drive the vision, strategy, and roadmap for AI-assisted test failure analysis across Windows Engineering Systems. Build Copilot- and MCP-powered experiences that analyze test artifacts, logs, telemetry, and error signal...

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Microsoft Redmond, WA,US, US Source published Sep 11, 2026 Verified 1 week ago
✓ 95% verification score · Source: Microsoft Careers · Always confirm final requirements on the original source.
Complete source information imported The available role or programme description, requirements, benefits and source facts were imported from the public official endpoint and formatted for reading.
Sr. Product Manager- Windows Engineering Systems opportunity at Microsoft
DeadlineWed Mar 10 3:40 AM 2027
EmploymentF U L L T I M E
CountryUS

Overview

Define and drive the vision, strategy, and roadmap for AI-assisted test failure analysis across Windows Engineering Systems. Build Copilot- and MCP-powered experiences that analyze test artifacts, logs, telemetry, and error signals to surface actionable root-cause insights directly within engineering workflows. Transform test failure analysis from a fragmented, manual investigation process into a streamlined experience that brings together relevant data, historical signals, and quality insights across engineering systems. Improve developer productivity by reducing investigation time, minimizing context switching, and integrating AI-assisted diagnostics into the engineering inner loop. Establish success metrics, AI evaluation frameworks, and quality measures focused on adoption, resolution effectiveness, signal quality, and reductions in no-repro outcomes. Partner across Engineering Syste

Full job description

Full Job Description

Define and drive the vision, strategy, and roadmap for AI-assisted test failure analysis across Windows Engineering Systems. Build Copilot- and MCP-powered experiences that analyze test artifacts, logs, telemetry, and error signals to surface actionable root-cause insights directly within engineering workflows. Transform test failure analysis from a fragmented, manual investigation process into a streamlined experience that brings together relevant data, historical signals, and quality insights across engineering systems. Improve developer productivity by reducing investigation time, minimizing context switching, and integrating AI-assisted diagnostics into the engineering inner loop. Establish success metrics, AI evaluation frameworks, and quality measures focused on adoption, resolution effectiveness, signal quality, and reductions in no-repro outcomes. Partner across Engineering Systems, Quality Engineering, Azure DevOps, and Copilot platform teams to deliver trusted AI solutions that improve validation quality and release confidence Bachelor's Degree AND 5+ years experience in product/service/program management or software development. OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: Bachelor's Degree AND 8+ years of experience in product management, program management, software engineering, or a related technical field, or equivalent experience. Experience delivering software products, engineering systems, developer tools, or platform services. Experience working on software quality, testing, validation, debugging, CI/CD systems, engineering infrastructure, or developer productivity solutions. Demonstrated ability to define product strategy and execute across multiple engineering organizations. Strong analytical skills and a data-driven approach to decision-making. Strong written, verbal, and executive communication skills. Experience building AI-powered products, copilots, agents, or LLM-based solutions. Experience defining AI evaluation frameworks, benchmarks, and quality measurement systems. Familiarity with Azure DevOps, CI/CD pipelines, test infrastructure, and engineering workflows. Experience designing or operating large-scale distributed systems and cloud services. Understanding of software validation, failure analysis, test automation, and debugging methodologies. Proven track record of leading ambiguous, cross-organizational initiatives that deliver measurable business impact.

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