Senior Hardware Engineer

Microsoft•Mountain View, CA

1d•$119,800 - $234,700

About The Position

The Senior Hardware Engineer will work within Microsoft’s Hardware, Infrastructure Management, and Fundamentals Engineering (HIFE) organization, supporting the readiness, quality, reliability, and operational excellence of Microsoft’s global Azure cloud infrastructure. The role applies systems expertise, telemetry, automation, AI, and agentic workflows to solve complex hardware, firmware, software, and system-level problems remotely and at scale. The engineer will turn deep engineering insights into scalable diagnostics, automation, intelligent workflows, and reusable capabilities that improve fleet readiness, reliability, diagnosability, and recoverability. The position emphasizes AI-native debugging, human-agent workflows, governed agentic execution, and closed-loop learning from incidents and engineering decisions. It also provides technical leadership and communicates evidence-based findings across engineering 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 those 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 actions, and addressing novel 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 without line authority across organizational boundaries, aligning partners on workflow ownership, interfaces, and decision rights, and adopting evidence-supported ideas from other teams.
Decision-ready written, visual, and live communication for engineering and executive audiences, 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 priorities, and setting a pace of change the team can absorb.

Responsibilities

Architect and deliver AI-native, end-to-end system-debug solutions for hyperscale datacenter infrastructure, combining engineering expertise, telemetry, automation, and agentic workflows.
Design and orchestrate human-agent engineering workflows for failure detection, triage, segmentation, root-cause analysis, mitigation, remediation, and learning.
Build specialized engineering agent skills, tools, and reusable workflows that codify expert debugging knowledge and enable autonomous or human-supervised execution at scale.
Lead cross-functional debugging and root-cause initiatives across hardware, firmware, software, platform, and datacenter systems, using agents and automation to reduce coordination and investigation overhead.
Own measurable improvements in system reliability and debugging effectiveness, including fault-isolation speed, time to root cause, defect reduction, automation coverage, and prevention of repeat failures.
Lead investigations of complex and ambiguous system failures, applying technical judgment when automated or agentic systems encounter novel scenarios, insufficient evidence, or conflicting signals.
Develop at-scale debugging methodologies, diagnostic strategies, validation techniques, and automated test routines for datacenter systems.
Engineer closed-loop learning systems that turn incidents, debugging evidence, root causes, and engineering decisions into reusable knowledge that improves 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 for mission-critical engineering workflows.
Drive the transition to outcome-driven engineering, where engineers set 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, identifying opportunities to improve Fleet Engineering at datacenter scale.
Communicate complex engineering insights, decisions, risks, and progress through data-driven narratives that support collaboration and decision-making across engineering teams, partners, and leadership.