Job description

Overview M365 Copilot inference is a high-impact engineering team advancing applied AI and large-scale machine learning across Microsoft. We design and operate the platform powering Microsoft 365 Copilot experiences, delivering intelligent capabilities to millions of users. Our team owns one of the world’s largest AI inference platforms, operating at massive GPU scale across global datacenters. We build the core LLM API and routing services that enable low-latency, highly available AI experiences, and continuously push the boundaries of performance, scalability, and efficiency. We are hiring a Principal Software Engineering Manager to lead a strategic initiative focused on maximizing throughput per GPU across the Copilot inference stack. This role is to drive inference engine efficiency by optimizing model execution and runtime performance, improving throughput per GPU, reducing cost per query, and unlocking capacity without additional hardware investment. 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 • Build and lead a high-performing engineering team focused on inference runtime efficiency and model execution performance. • Define and drive strategy to improve throughput per GPU through runtime optimizations. • Increase engineering agility, enabling faster experimentation, iteration, and rollout of performance improvements. • Partner across M365 Core, AI Core, Azure, and Microsoft Research to co-design and productionize advanced inference optimizations. • Establish metrics, telemetry, and experimentation frameworks to measure efficiency gains and guide investment decisions. • Own live-site performance, reliability, and operational excellence for inference engines at scale. • Drive alignment across partner teams on engine interfaces, performance goals, and optimization priorities. Qualifications Required Qualifications: • Bachelor'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, 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 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python • OR Bachelor's Degree in Computer Science or related technical field AND 15+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python • OR equivalent experience. • 6+ years people management experience. • Experience leading engineering teams building backend or distributed systems. • Hands-on experience improving system throughput, performance, and resource utilization across large-scale infrastructure. • Systems thinking, with the ability to identify and optimize bottlenecks across execution, scaling, and resource management. • Experience driving system-level improvements in areas such as workload execution, scheduling, batching, or infrastructure efficiency. • Able to translate technical insights into clear engineering priorities and execution plans. • Comfortable collaborating across teams to align on goals and execution. • Experience with developing AI/ML inference systems or GPU-based workloads. • Familiarity with inference or training runtime optimization techniques. • Experience improving throughput per resource (e.g., cost per query) in large-scale systems. Software Engineering M6 - The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 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 $220,800 - $331,200 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

Build and lead a high-performing engineering team focused on inference runtime efficiency and model execution performance.
Define and drive strategy to improve throughput per GPU through runtime optimizations.
Increase engineering agility, enabling faster experimentation, iteration, and rollout of performance improvements.
Partner across M365 Core, AI Core, Azure, and Microsoft Research to co-design and productionize advanced inference optimizations.
Establish metrics, telemetry, and experimentation frameworks to measure efficiency gains and guide investment decisions.
Own live-site performance, reliability, and operational excellence for inference engines at scale.
Drive alignment across partner teams on engine interfaces, performance goals, and optimization priorities.
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.
OR equivalent experience.
6+ years people management experience.
Experience leading engineering teams building backend or distributed systems.
Hands-on experience improving system throughput, performance, and resource utilization across large-scale infrastructure.
Systems thinking, with the ability to identify and optimize bottlenecks across execution, scaling, and resource management.
Experience driving system-level improvements in areas such as workload execution, scheduling, batching, or infrastructure efficiency.
Able to translate technical insights into clear engineering priorities and execution plans.
Comfortable collaborating across teams to align on goals and execution.
Experience with developing AI/ML inference systems or GPU-based workloads.
Familiarity with inference or training runtime optimization techniques.
Experience improving throughput per resource (e.g., cost per query) in large-scale systems.
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

None flagged.

Application prep

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

Keywords to mirror

aimachine learningllmapiazurejavajavascriptpythonml

Platform: eightfold
Effort: medium