채용
보상
$119,800 - $234,700
필수 스킬
C
C++
C#
Java
JavaScript
Python
Overview
CoreAI is at the forefront of Microsoft’s mission to redefine how software is built and experienced. We are responsible for building the foundational platforms, services, programming models, and developer experiences that power the next generation of applications using Generative AI. Our work enables developers and enterprises to harness the full potential of AI to create intelligent, adaptive, and transformative software.
The AI Core Infrastructure team, part of AI Platform team in CoreAI Organization is responsible for large-scale, highly reliable and efficient GPU management infrastructure and the inference and training platforms that powers all of Microsoft’s AI workloads, such as M365 Co Pilot, Github Co Pilot, Microsoft Co Pilot, AI Foundry’s Inference and Fine-Tuning offering of OAI and OSS models, and many more.
As a Senior Software Engineer on the training infrastructure team, you will work on cutting edge infrastructure and tools to support large scale model pre-training, post-training and fine-tuning on latest generation of NVIDIA and AMD GPUs in Azure and Microsoft partner clouds on some of the world’s largest AI Supercomputers.
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.
In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.
Responsibilities
As a senior engineer on the training infrastructure team, your responsibilities include:
-
Architect, design, and develop core AI Infrastructure services developed in Go, Rust, Python, C++, and C# deployed on large-scale Kubernetes clusters to support pre-training and post-training of state-of-the-art LLMs, SLMs, multimodal, and code-specific models.
-
Collaborate closely with engineers, researchers and external partners to debug, diagnose, and improve stability of large-scale training runs.
-
Enhance systems and applications to deliver high stability, low latency, strong security, and maintainability in large-scale complex training environments in Azure and in partner clouds.
-
Provide operational support, technical leadership, and vision while contributing to the deployment, monitoring, and continuous improvement of engineering systems and practices.
Qualifications Required Qualifications:
- Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python- OR equivalent experience.
Other Requirements:
- Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years
Preferred Qualifications:
-
3+ years designing, developing, and shipping high quality software.
-
2+ years of experience with distributed systems and cloud-based infrastructure.
-
1+ year of experience with DevOps practices (CI/CD, automated testing, deployment, etc.).
-
4+ years of software development experience in C#, C++, Python, or similar languages.
-
2+ years of experience with containerization tools (e.g., Docker, Kubernetes).
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Knowledge and hands on experience with production ML systems, large-scale training infrastructure, NCCL, CUDA libraries and tools.
#CoreAI
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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1
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0
모의 지원자 수
0
스크랩
0
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Microsoft 소개

Microsoft
PublicMicrosoft Corporation is an American multinational technology conglomerate headquartered in Redmond, Washington.
10,001+
직원 수
Redmond
본사 위치
$3000B
기업 가치
리뷰
3.8
5개 리뷰
워라밸
4.1
보상
4.3
문화
3.4
커리어
3.2
경영진
3.0
65%
친구에게 추천
장점
Excellent compensation and benefits package
Four-day workweek with improved work-life balance
Supportive managers and teams
단점
High-pressure environment causing anxiety
Unprofessional interview processes
Limited creative work opportunities
연봉 정보
5,620개 데이터
Senior/L5
Senior/L5 · Account Management
5개 리포트
$209,483
총 연봉
기본급
$181,941
주식
-
보너스
-
$194,895
$209,483
면접 경험
1개 면접
난이도
4.0
/ 5
소요 기간
14-28주
경험
긍정 0%
보통 0%
부정 100%
면접 과정
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
자주 나오는 질문
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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