Job description

Overview The FIT Infrastructure team builds the foundational accelerated compute platforms that power large scale AI training and inference for Microsoft. Our mission is to deliver secure, reliable, and highly efficient GPU infrastructure that enables multi-tenant AI systems at global scale while maximizing utilization, availability, performance, and reliability.  This role sits at the intersection of cloud infrastructure, distributed systems software, and frontier AI model serving, working closely with Azure Infrastructure, Microsoft Foundry, Microsoft AI and Microsoft 365 teams to deliver end-to-end platform capabilities. As a a Principal Software Engineering, AI Infrastructure, you will work on mission critical infrastructure that directly powers largescale AI systems. Influence the future of cloud GPU platforms used by internal and external customers and ollaborate with experts across hardware, networking, and AI deployment and serving software. This a an opportunity to grow as a technical leader, shaping long term platform strategy.  Microsoft’s mission is to empower every person and every organization on the planet to achieve more, and we’re dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their best each day. Join us and help shape the future of the world.  Responsibilities • Develop systems for AI Workload management, scheduling, preemption, and failover.  • Develop systems for AI Workload serving.  • Design and build GPU accelerated infrastructure for training and inference workloads, spanning bare metal, virtual machines, and containerized environments.  • Build and operate advanced orchestration and resource governance scenarios using platforms such as AKS and related Kubernetes ecosystem capabilities to enable fair sharing, isolation, and efficient utilization of accelerated resources.  • Design, Develop, test, and maintain services written in C#, Golang hosted on Kubernetes clusters.  • Optimize performance, reliability, and utilization across large GPU fleets, including scaleup and scale out configurations.  • Drive end-to-end platform features from design through production, including observability, diagnostics, and operational excellence.  • Influence platform architecture and technical direction across teams through design reviews and technical leadership.  • Embody our Culture and Values 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, Linux, Go 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 thereafter. 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, Linux, Go 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, linux, Go or Python • OR equivalent experience. • Proven ability to design and operate large scale, production infrastructure with high reliability and performance requirements.  • Demonstrated problem-solving skills and the ability to debug complex, cross layer systems issues.  • Demonstrated technical leadership, including mentoring engineers and driving cross team architectural alignment.  • Hands-on experience with resource scheduling, preemption, priority handling on large clusters.  • Advanced collaboration and communication skills, with the ability to work across organizational boundaries.  • Experience with large-scale online Inferencing services.  • Experience in building or operating multitenant AI platforms in cloud environments.  • Familiarity with inference and/or distributed training stacks (e.g., NCCL style collectives, model/data parallelism, Large-Language Models).  • Familiarity with GPU accelerated computing (e.g., CUDA, GPU drivers, device plugins, or runtime integration). • Dev-ops experience with microservices architecture in a complex infrastructure and operational environment. #AIINFRA 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: careers.microsoft.com 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.

Requirements

Must-haves

Develop systems for AI Workload management, scheduling, preemption, and failover.
Develop systems for AI Workload serving.
Design and build GPU accelerated infrastructure for training and inference workloads, spanning bare metal, virtual machines, and containerized environments.
Design, Develop, test, and maintain services written in C#, Golang hosted on Kubernetes clusters.
Optimize performance, reliability, and utilization across large GPU fleets, including scaleup and scale out configurations.
Drive end-to-end platform features from design through production, including observability, diagnostics, and operational excellence.
Influence platform architecture and technical direction across teams through design reviews and technical leadership.
Embody our Culture and Values
Other Requirements
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Nice-to-haves

OR equivalent experience.
Proven ability to design and operate large scale, production infrastructure with high reliability and performance requirements.
Demonstrated problem-solving skills and the ability to debug complex, cross layer systems issues.
Demonstrated technical leadership, including mentoring engineers and driving cross team architectural alignment.
Hands-on experience with resource scheduling, preemption, priority handling on large clusters.
Advanced collaboration and communication skills, with the ability to work across organizational boundaries.
Experience with large-scale online Inferencing services.
Experience in building or operating multitenant AI platforms in cloud environments.
Familiarity with inference and/or distributed training stacks (e.g., NCCL style collectives, model/data parallelism, Large-Language Models).
Familiarity with GPU accelerated computing (e.g., CUDA, GPU drivers, device plugins, or runtime integration).
Dev-ops experience with microservices architecture in a complex infrastructure and operational environment.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiazurekubernetesgolangjavajavascriptgopython

Platform: eightfold
Effort: medium