Senior Hardware Engineer

Microsoft•Redmond, WA

1d•$119,800 - $261,000

About The Position

Microsoft’s Hardware, Infrastructure Management, and Fundamentals Engineering organization supports hardware readiness, quality, reliability, and operational excellence across its cloud infrastructure. Within Fleet Engineering, the Senior Hardware Engineer solves complex hardware, firmware, software, and system-level problems across the Global Azure Cloud using remote, at-scale engineering capabilities. The role translates engineering insights into scalable diagnostics, automation, intelligent workflows, and reusable capabilities that improve fleet readiness, reliability, diagnosability, and recoverability. The engineer develops AI-native system-debug solutions and human-agent workflows for hyperscale datacenter infrastructure, and leads investigations and root-cause initiatives across engineering disciplines. The role also advances governed agentic execution, closed-loop learning, diagnostic methodologies, and outcome-driven engineering, while communicating technical findings and progress to teams, partners, and leadership.

Requirements

Master’s degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or a related field and 3+ years of technical engineering experience; OR bachelor’s degree in one of these fields or a related field and 5+ 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

Master’s degree in Computer Science or a related technical field and 6+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python; OR bachelor’s degree in Computer Science or a related technical field and 8+ years of such experience.
Experience with AI agents in production operations, including setting intent and success criteria, evaluating outcomes, curating agent memory, governing agent capabilities, and handling unfamiliar problems.
Analytical rigor with large-scale telemetry and data tools, including clearly defining metrics, distinguishing measured results from targets or 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-supported ideas from other teams.
Ability to communicate decision-ready information to engineers and executives in writing, visuals, and live forums, including 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 priorities, and setting an absorbable pace of change.

Responsibilities

Architect and deliver AI-native, end-to-end system-debug solutions for hyperscale datacenter infrastructure using engineering expertise, telemetry, automation, and agentic workflows.
Design and orchestrate human-agent workflows for failure detection, triage, segmentation, root-cause analysis, mitigation, remediation, and learning.
Build specialized agentic engineering skills, tools, and reusable workflows that codify debug knowledge and support autonomous or human-supervised execution at scale.
Lead cross-functional debug and root-cause initiatives across hardware, firmware, software, platform, and datacenter systems.
Own measurable improvements in reliability and debug effectiveness, including fault-isolation speed, time to root cause, defect reduction, automation coverage, and prevention of repeat failures.
Investigate complex and ambiguous system failures using technical judgment when automated systems encounter novel scenarios, insufficient evidence, or conflicting signals.
Develop at-scale debug methodologies, diagnostic strategies, validation techniques, and automated test routines for datacenter systems.
Engineer closed-loop learning systems that turn incidents, evidence, root causes, and engineering decisions into reusable knowledge for agents, automation, diagnostics, and platform readiness.
Define safe and governed agentic execution models, including human-in-the-loop controls, validation, observability, permissions, and escalation paths.
Drive the transition to outcome-driven engineering, with engineers setting intent and guardrails while agents perform appropriate investigation, correlation, orchestration, and repetitive execution.
Provide technical leadership on emerging AI, agent, diagnostic, telemetry, and automation technologies and identify opportunities to improve Fleet Engineering at datacenter scale.
Communicate engineering insights, decisions, risks, and progress through data-driven narratives for collaboration and decision-making.