Job description

Overview Every time someone uses Azure OpenAI Service, their request needs to reach the right graphics processing unit (GPU), in the right region, at the right moment. Our team makes that happen. We build the platform that decides how AI traffic flows across Microsoft's global infrastructure, so services like Azure OpenAI and Microsoft Copilot stay fast and reliable. You'll join a supportive, connected team that keeps these experiences fair and dependable for every customer while making the most of every GPU we run. As a Senior Software Engineer, AI Infrastructure on our team, you'll design, build, and run services that route and prioritize AI requests and match them to available computing capacity. You'll own the design of key parts of our platform, work with partner teams to bring new AI models and capabilities to customers, and help keep our services healthy in production. This opportunity will allow you to deepen your knowledge of distributed systems at global scale, learn how the world's leading AI models are served, and grow as a technical leader and mentor on your team. 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 • Design - Own the design of key parts of our platform for routing AI traffic and managing computing capacity. Write design proposals, compare options, and make sure designs meet goals for speed, reliability, scale, and cost. • Coding - Write clear, secure, well-tested code and set a good example for the team. Give thoughtful, timely code reviews that help others grow. • Implement - Break work into clear, dependable milestones with program managers. Use feature flags and gradual rollouts to test changes safely, measure their impact, and roll back quickly when needed. • Reliability and Supportability - Take part in the on-call rotation as a designated responsible individual (DRI). Add monitoring and dashboards, improve troubleshooting guides, and help reduce how often incidents happen. • Understand User Requirements - Work with product managers and partner teams to understand what customers need and turn those needs into clear technical requirements. • Engineering Excellence - Build security, privacy, and compliance into every stage of your work, from design to deployment. Automate testing and releases, and share what you learn with the team. • AI-Native Development - Use artificial intelligence (AI) tools across the software development lifecycle (SDLC), from writing code and tests to investigating live issues. Take responsibility for AI-generated work and help teammates do the same. • Embody our Culture and Values Qualifications Required Qualifications: • Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience building distributed systems with coding in languages including, but not limited to, C#, Java, or Go • 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 6+ years technical engineering experience building distributed systems with coding in languages including, but not limited to, C#, Java, or Go • OR Bachelor's Degree in Computer Science or related technical field • AND 8+ years technical engineering experience building distributed systems with coding in languages including, but not limited to, C#, Java, or Go • OR equivalent experience. • 3+ years of experience running distributed services on public cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP). • 2+ years of experience with systems that route or schedule traffic, or that allocate computing resources. • Experience with Python, Docker, and Kubernetes. • Experience with AI or machine learning (ML) infrastructure, such as serving models in production or managing GPU fleets. • Experience delivering projects with partner teams, from design through launch and operation. • Experience being on call for highly available services and improving monitoring to reduce incidents. #AIINFRA 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 $160,200 - $261,000 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

Coding - Write clear, secure, well-tested code and set a good example for the team. Give thoughtful, timely code reviews that help others grow.
Implement - Break work into clear, dependable milestones with program managers. Use feature flags and gradual rollouts to test changes safely, measure their impact, and roll back quickly when needed.
Reliability and Supportability - Take part in the on-call rotation as a designated responsible individual (DRI). Add monitoring and dashboards, improve troubleshooting guides, and help reduce how often incidents happen.
Understand User Requirements - Work with product managers and partner teams to understand what customers need and turn those needs into clear technical requirements.
Engineering Excellence - Build security, privacy, and compliance into every stage of your work, from design to deployment. Automate testing and releases, and share what you learn with the team.
Embody our Culture and Values
Other Requirements
3+ years of experience running distributed services on public cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).
2+ years of experience with systems that route or schedule traffic, or that allocate computing resources.
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.
Experience with Python, Docker, and Kubernetes.
Experience with AI or machine learning (ML) infrastructure, such as serving models in production or managing GPU fleets.
Experience delivering projects with partner teams, from design through launch and operation.
Experience being on call for highly available services and improving monitoring to reduce incidents.

Application prep

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

Keywords to mirror

azureaijavagoawsgcppythondockerkubernetesmachine learningml

Platform: eightfold
Effort: medium