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Overview The AI Infrastructure team is responsible for building and operating large-scale, highly reliable, and efficient GPU infrastructure that powers Microsoft’s AI ecosystem. We host the training and inference platforms behind many of Microsoft’s flagship AI offerings, including Microsoft 365 Copilot, GitHub Copilot, Microsoft Copilot, and Azure AI Foundry’s inference and fine-tuning services for both OpenAI and open-source models. Our infrastructure enables AI innovation at hyperscale and supports some of the most demanding workloads across the company. As a Software Engineer on the AI infrastructure team, you will work on cutting edge infrastructure and tools to support large scale model deployments, 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. Responsibilities As an engineer on the AI infrastructure team, your responsibilities include: Design, develop, and maintain AI infrastructure services in Go, Rust, Python, C++, and C#, deployed on large-scale Kubernetes clusters to support inference, pre-training, and post-training workloads for state-of-the-art AI models. Collaborate with engineers, researchers, and external partners to troubleshoot issues, improve reliability, and optimize the performance of large-scale AI training and inference systems. Build and enhance distributed systems that deliver high reliability, low latency, operational efficiency, and strong security across Azure and partner cloud environments. Develop automation and tooling to improve GPU capacity utilization, streamline fleet operations, and enable efficient scaling of AI infrastructure. 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 2+ 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 2+ 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.). 2+ years of software development experience in C#, C++, Python, or similar languages. 2+ years of experience with containerization tools (e.g., Docker, Kubernetes). Knowledge and hands on experience with production ML systems, large-scale training infrastructure, NCCL, CUDA libraries and tools #AIINFRA Software Engineering IC3 - The typical base pay range for this role across the U.S. is USD $102,100 - $202,200 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 $133,800 - $219,200 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.