Principal Hardware Engineer

Microsoft • Redmond, WA

2d • $142,800 - $304,200

About The Position

Microsoft Silicon, Cloud Hardware, and Infrastructure Engineering (SCHIE) develops and operates infrastructure and foundational technologies supporting Microsoft’s cloud and online services. The Hardware, Infrastructure Management, and Fundamentals Engineering (HIFE) team defines and delivers operational measures for hardware manufacturing, improving planning, quality, delivery, scale, and sustainability for Microsoft cloud hardware.

The Principal Hardware Engineer will lead development of end-to-end agentic and automated debugging solutions for datacenter fleet health systems. The role drives cross-functional root-cause analysis and defect-reduction workflows across software, firmware, hardware components, racks, and fleets. It is accountable for the quality and reliability of fleet-health agents and for developing scalable debugging methods and automation for mission-critical services.

Requirements

• Master's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or a related field and 7+ years of technical engineering experience; or bachelor's degree in one of these fields or a related field and 8+ years of technical engineering experience; or equivalent experience.
• Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

Nice To Haves

• 10+ years of technical leadership and a record of owning a measured reliability, quality, capacity, or recovery outcome at production scale using reliability engineering methods and working across hardware, firmware, and software teams.
• Deep expertise in at least one server or datacenter infrastructure layer, such as CPU or SoC architecture, memory subsystem, storage, networking, virtualization, firmware, cloud-native operating systems, or fleet telemetry and data systems, with working fluency in how failures propagate.
• Proven ability to lead investigations of previously unsolved failures at scale, test competing hypotheses, preserve evidence before repair, identify root cause across layers, and convert findings into permanent fixes, runbooks, tests, or automated checks.
• Experience setting intent and limits for automated or AI-driven systems, defining success criteria and evidence standards, evaluating results against outcomes, determining what may act without prior human approval, and ensuring rollback, audit trails, and accountable ownership.
• Experience with AI agents in production operations, including setting intent and success criteria, evaluating outputs against outcomes, curating agent memory, governing agent actions, and addressing unfamiliar problems.
• Analytical rigor with large-scale telemetry and data tools, including defining metrics consistently, distinguishing measured results from targets, proxies, and estimates, and counting distinct machines rather than retries.
• Experience leading across organizational boundaries without line authority, aligning partners on workflow ownership, interfaces, and decision rights, and adopting evidence-based ideas from other teams.
• Ability to communicate decision-ready information to engineers and executives in writing, visuals, and live forums, including technical reports that state conclusions, evidence, confidence, and what could change the answer.
• Planning discipline in a fast-changing environment, including working in small reversible steps, adapting to evidence, managing time and priorities, and setting an absorbable pace of change.

Responsibilities

• Lead development and implementation of end-to-end agentic and automated debug solutions for datacenter fleet health systems at scale.
• Lead collaboration across hardware, firmware, and software teams on end-to-end root-cause analysis, defining the rules agents operate within and clarifying handoffs and agent responsibilities.
• Own execution of targeted agentic defect-reduction workflows for novel faults across software, blade components, racks, and fleets, and convert fixes into reusable lessons.
• Resolve complex problems using technical and business understanding; set goals, specifications, and evidence standards for agents to reduce late-detected errors.
• Develop scalable debugging methodologies for mission-critical services and build automation to improve debugging efficiency.
• Own fleet-health agent quality and reliability, run persona swarms safely, and set runtime limits for agentic support during incident response and recovery.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal