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Hardware Health

Reposted Yesterday
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Remote
Hiring Remotely in United States
143K-331K Annually
Senior level
Remote
Hiring Remotely in United States
143K-331K Annually
Senior level
Design and implement hardware health monitoring, predictive analytics, and diagnostics for exascale GPU clusters. Lead incident triage, define health KPIs, collaborate with silicon/firmware/datacenter teams, and drive automation to improve reliability and reduce manual interventions.
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Overview

Microsoft AI operates one of the world’s most advanced AI training infrastructures, featuring multi-gigawatt clusters spanning tens of thousands of high-performance GPUs, ultra-low-latency NVLink/NVSwitch networks, and innovative liquid-cooling systems. Our team is seeking a Member of Technical Staff, Hardware Health, to ensure these systems deliver sustained reliability, performance, and availability across exascale-class deployments. 

We work closely with research, hardware, datacenter, and platform engineering teams to develop predictive health models, failure detection frameworks, and autonomous remediation systems that keep our AI clusters operating at frontier scale. 

Our newly formed organization, Microsoft AI, is dedicated to advancing Copilot and other consumer AI products and research. The team is responsible for Copilot, Bing, Edge, and generative AI research. Join us and help shape the future of personal computing. 

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees, we embrace a growth mindset, innovate to empower others, and collaborate to achieve shared goals. Every day, we build on our values of respect, integrity, and accountability to foster a culture of inclusion where everyone can thrive at work and beyond. 

MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction. 


Responsibilities
  • Design and develop next-generation hardware health monitoring and diagnostic frameworks for large GPU clusters (NVL16/NVL72/GB200+ scale).
  • Build predictive analytics pipelines leveraging telemetry, power, and thermal data to anticipate hardware degradation and systemic issues.
  • Collaborate with silicon, firmware, and datacenter engineers to identify root causes and remediate large-scale hardware anomalies.
  • Define system health KPIs (e.g., NIS/RIS, MTBF, failure domain analysis) and integrate them into real-time observability platforms.
  • Lead incident triage for high-impact GPU, network, and cooling issues across distributed clusters.
  • Drive automation in health management to reduce manual intervention to the top 5% of anomalies.
  • Partner with cross-functional teams to influence hardware design for reliability, thermal efficiency, and serviceability.

Qualifications

Required Qualifications: 

  • Bachelor'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 equivalent experience.

Preferred Qualifications: 

  • Master'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 OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python 
    • OR equivalent experience.
  • Experience working with large-scale HPC or GPU systems (NVIDIA H100/GB200 or equivalent).
  • Deep understanding of GPU architecture, high-speed interconnects (NVLink, InfiniBand, RoCE), and large datacenter topologies.
  • Proficiency in hardware telemetry, diagnostics, or failure analysis tools.
  • Experience with exascale-class systems or cloud-scale AI clusters.
  • Familiarity with reliability modeling, machine learning-based anomaly detection, or predictive maintenance.
  • Contributions to large-scale infrastructure operations, supercomputing centers, or AI hardware design. 

Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 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 $188,000 - $304,200 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

Software Engineering IC6 - The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 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 $220,800 - $331,200 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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