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Senior Hardware Engineer

Microsoft · CA, United States · WA, United States · TX, United States · NC, United States · OR, United States · Mountain View, CA, United States · Redmond, WA, United States · Austin, TX, United States · Raleigh, NC, United States · Hillsboro, OR, United States · Mountain View, United States · Redmond, United States · Austin, United States · Raleigh, United States · Hillsboro, United States · $119,800 to $261,000 USD / year

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Microsoft

Senior Hardware Engineer

CA, United States
WA, United States
TX, United States
NC, United States
OR, United States
Mountain View, CA, United States
Redmond, WA, United States
Austin, TX, United States
Raleigh, NC, United States
Hillsboro, OR, United States
Mountain View, United States
Redmond, United States
Austin, United States
Raleigh, United States
Hillsboro, United States

$119,800 to $261,000 USD / year

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Job details

Pay: $119,800 to $261,000 USD / year
Apply by: May 4, 2027
Posting language: English
Posted: Nov 5, 2026

Original job description

Overview

As Microsoft’s cloud continues to scale, our ability to deploy new hardware platforms and infrastructure quickly, reliably, and cost effectively is critical. The Hardware, Infrastructure Management, and Fundamentals Engineering (HIFE) organization drives hardware readiness, quality, reliability, and operational excellence across Microsoft’s cloud infrastructure. Fleet Engineering is transforming how complex infrastructure problems are solved by combining systems expertise with telemetry, automation, AI, and agentic workflows. We are looking for a Sr. Hardware Engineer to solve challenging hardware, firmware, software, and system-level problems across Microsoft’s Global Azure Cloud enabled by remote, at-scale engineering capabilities. The role turns deep engineering insights into scalable diagnostics, automation, intelligent workflows, and reusable capabilities that improve Fleet readiness, reliability, diagnosability, and recoverability.

#azurehwjobs #HIFE #SCHIE

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 that accelerate failure detection, triage, segmentation, root cause analysis, mitigation, remediation, and learning.
Build specialized engineering agentic skills, tools, and reusable workflows that codify expert debug knowledge and enable autonomous or human-supervised execution at scale.
Lead cross-functional debug and root-cause initiatives spanning hardware, firmware, software, platform, and datacenter systems, using agents and automation to reduce coordination and investigation overhead.
Own measurable improvements in system reliability and debug effectiveness, including faster fault isolation, reduced time to root cause, defect reduction, automation coverage, and prevention of repeat failures.
Lead investigation of complex and ambiguous system failures, applying deep technical judgment where automated or agentic systems encounter novel scenarios, insufficient evidence, or conflicting signals.
Develop world-class at-scale debug methodologies, diagnostic strategies, validation techniques, and automated test routines for datacenter systems.
Engineer closed-loop learning systems that convert incidents, debug evidence, root causes, and engineering decisions into reusable knowledge that continuously improves agents, automation, diagnostics, and future 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 from engineer-driven execution to outcome-driven engineering, where engineers define 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 fundamentally improve how Fleet Engineering operates at datacenter scale.
Communicate complex engineering insights, decisions, risks, and progress through data-driven narratives that enable effective collaboration and decision-making across engineering teams, partners, and leadership.

Qualifications

Required Qualifications:

Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years technical engineering experience OR equivalent experience

Other Requirements:

Ability to meet Microsoft, customer, and/or government security screening requirements is required for this role. These requirements include, but are not limited to, the following specialized security screening: Microsoft Cloud Background Check. This position is required to pass the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

Preferred Qualifications:

Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
Experience with AI agents in production operations: setting intent and success criteria, evaluating output against outcomes, curating the memory they rely on, governing what each may do, and solving the problems they have not seen.
Analytical rigor with large-scale telemetry and data tools: defining each metric once (population, start and end events, denominator), separating measured results from targets, proxies and estimates, and counting distinct machines, not retries.
Experience leading without line authority across organizational boundaries: aligning partners on workflow ownership, interfaces and decision rights, and adopting ideas from other teams on evidence.
Decision-ready communication for engineers and executives in writing, visuals and live forums, including technical reports that state the conclusion, the evidence, the confidence and what would change the answer.
Planning discipline in a fast-changing environment: works in small, reversible steps, adapts as evidence changes, manages time and priorities, and sets a pace of change the team can absorb.

Hardware Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

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More about this job

Skills

Hardware platforms
Root cause analysis
Firmware
Leadership
Automation technology
Decision making
Electrical engineering
Computer engineering
Mechanical engineering
Computer science
C
C++
C#
Oracle Java
JavaScript
Python
Workflow
Communication skills

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