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Member of Technical Staff, Site Reliability Engineer (HPC) - MAI SuperIntelligence Team
United States, California, Mountain View
·
On-site
·
Full-time
·
2mo ago
報酬
$139,900 - $274,800
福利厚生
•Equity
必須スキル
Site Reliability Engineering
DevOps
Infrastructure Engineering
Overview:
As Microsoft continues to push the boundaries of AI, we are on the lookout for passionate individuals to work with us on the most interesting and challenging AI questions of our time. Our vision is bold and broad — to build systems that have true artificial intelligence across agents, applications, services, and infrastructure. It’s also inclusive: we aim to make AI accessible to all — consumers, businesses, developers — so that everyone can realize its benefits.
We’re looking for an experienced HPC Site Reliability Engineer (SRE) to join our High Performance Computing (HPC) infrastructure team. In this role, you’ll blend software engineering and systems engineering to keep our large-scale distributed AI infrastructure reliable and efficient. You’ll ensure that AI systems stay efficient and reliable with very high uptimes.
Microsoft Superintelligence Team Microsoft Superintelligence team’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. This role is part of Microsoft AI's Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values. Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models! Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Starting January 26, 2026, 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- Reliability & Availability: Ensure uptime, resiliency, and fault tolerance of HPC clusters powering MAI model training and inference.
- Observability: Design and maintain monitoring, alerting, and logging systems to provide real-time visibility into all aspects of HPC systems including GPU, clusters, storage and networking.
- Automation & Tooling: Build automation for deployments, incident response, scaling, and failover in CPU+GPU environments.
- Incident Management: Lead on-call rotations, troubleshoot production issues, conduct blameless postmortems, and drive continuous improvements.
- Security & Compliance: Ensure data privacy, compliance, and secure operations across model training and serving environments.
- Collaboration: Partner with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows.
Qualifications:
Required Qualifications
- Master's Degree in Computer Science, Information Technology, or related field AND 2+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure EngineeringOR Bachelor's Degree in Computer Science, Information Technology, or related field AND 4+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering
- OR equivalent experience
Preferred Qualifications
- Strong proficiency in Kubernetes, Docker, and container orchestration.
- Knowledge of CI/CD pipelines for Inference and ML model deployment.
- Hands-on experience with public cloud platforms like Azure/AWS/GCP and infrastructure-as-code.
- Expertise in monitoring & observability tools (Grafana, Datadog, OpenTelemetry, etc.).
- Strong programming/scripting skills in Python, Go, or Bash.
- Solid knowledge of distributed systems, networking, and storage.
- Experience running large-scale GPU clusters for ML/AI workloads (preferred).
- Familiarity with ML training/inference pipelines.
- Experience with high-performance computing (HPC) and workload schedulers ( Kubernetes operators).
- Background in capacity planning & cost optimization for GPU-heavy environments.
- Work on cutting-edge infrastructure that powers the future of Generative AI.
- Collaborate with world-class researchers and engineers.
- Impact millions of users through reliable and responsible AI deployments.
- Competitive compensation, equity options, and comprehensive benefits.
Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $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 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 $158,400 - $258,000 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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Microsoftについて

Microsoft
PublicMicrosoft Corporation is an American multinational technology conglomerate headquartered in Redmond, Washington.
10,001+
従業員数
Redmond
本社所在地
$3000B
企業価値
レビュー
3.8
5件のレビュー
ワークライフバランス
4.1
報酬
4.3
企業文化
3.4
キャリア
3.2
経営陣
3.0
65%
友人に勧める
良い点
Excellent compensation and benefits package
Four-day workweek with improved work-life balance
Supportive managers and teams
改善点
High-pressure environment causing anxiety
Unprofessional interview processes
Limited creative work opportunities
給与レンジ
5,620件のデータ
Senior/L5
Senior/L5 · Account Management
5件のレポート
$209,483
年収総額
基本給
$181,941
ストック
-
ボーナス
-
$194,895
$209,483
面接体験
1件の面接
難易度
4.0
/ 5
期間
14-28週間
体験
ポジティブ 0%
普通 0%
ネガティブ 100%
面接プロセス
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
よくある質問
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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