Expert/Leader Leads the technical vision, architecture, and execution of AI performance, framework, benchmarking, and developer-productivity initiatives. Drives cross-team architecture, resolves complex systems trade-offs, mentors technical leaders, and contributes hands-on through prototyping, implementation, reviews, debugging, observability, and automation. Delivers measurable improvements in model onboarding speed, runtime performance, reliability, hardware utilization, and Azure capacity efficiency across models, frameworks, compilers, runtimes, systems, services, and hardware. The summary above was generated by AI Overview

The Artificial Intelligence Performance team at Microsoft develops AI software that enables running AI models efficiently. We collaborate with our hardware teams and partners, both internal and external, and operate at the intersection of AI algorithmic innovation, purpose-built AI hardware, systems, and software. We are a team of highly capable and motivated people that pride themselves on a collaborative and inclusive culture. We own inference performance of OpenAI and other state of the art LLM models and work directly with OpenAI on the models hosted on the Azure OpenAI service serving some of the largest workloads on the planet with trillions of inferences per day in major Microsoft products, including Office, Windows, Bing, SQL Server, and Dynamics.

This is a hands-on technical leadership role for an engineer who can establish direction and lead teams through ambiguous, end-to-end systems challenges. Successful candidates combine strong software engineering fundamentals with deep curiosity about AI workloads, use disciplined measurement to guide decisions, and create alignment across organizational boundaries to deliver measurable production impact.

As a Principal Software Engineer - Performance, you will serve as a technical lead for high-impact initiatives spanning AI frameworks, performance engineering, benchmarking, and tooling. You will establish technical direction, drive architectural decisions, and lead engineers across teams through the design and delivery of solutions that improve model velocity, platform efficiency, and production reliability. You will also identify systemic opportunities, align stakeholders around durable architectures and success measures, and remain hands-on in critical areas of implementation.

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

• Own the technical vision, architecture, roadmap, and multi-release execution strategy for critical AI framework, performance, benchmarking, or developer-productivity capabilities.

• Lead technical alignment across teams by shaping architecture and priorities, resolving competing requirements, and holding researchers, product groups, infrastructure owners, and hardware partners accountable to clear decisions, execution plans, and measurable outcomes.

• Build technical leadership capacity across the organization by mentoring engineers, developing emerging technical leads, establishing high standards for quality and maintainability, and creating an inclusive engineering culture in which teams can execute effectively at scale.

• Steer ambiguous, cross-stack investigations and investments spanning models, frameworks, compilers, runtimes, systems, services, and silicon; set technical priorities, resolve trade-offs, and drive teams to decisions.

• Provide hands-on technical leadership by setting the engineering approach, delegating and unblocking critical work, and directly contributing through prototypes, critical-path implementation, design and code reviews, complex debugging, and operational readiness.

• Spearhead common measurement, automation, observability, and engineering mechanisms that turn one-off analyses into scalable platform capabilities.

• Drive measurable improvements in model onboarding velocity, runtime performance, reliability, hardware utilization, and Azure capacity efficiency.

• Bachelor’s Degree in Computer Science or a related technical field and 8+ years of technical engineering experience coding in languages such as C++ or Python, *

• OR equivalent experience., * 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., * 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++ 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++ or Python

*

• OR equivalent experience.

• Demonstrated experience serving as a technical lead for complex cross-team initiatives, including setting strategy, driving architectural decisions, aligning stakeholders, and driving execution from concept through production.

• Robust/extensive experience designing and shipping complex, high-performance or distributed software systems

• Deep foundation in software architecture, computer architecture, and accelerator-aware optimization

• Advanced experience with AI/ML workloads and frameworks

• Proven track record of creating reusable platforms, influencing senior technical and business stakeholders, developing other technical leaders, and scaling engineering practices across teams.

AIInfra

Software Engineering IC6 - 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.

In-Office or Remote 2 Locations 272K-431K Annually Expert/Leader 272K-431K Annually Expert/Leader Artificial Intelligence * Computer Vision * Hardware * Robotics * Metaverse Lead end-to-end GPU and systems performance engineering for NVIDIA CSP and hyperscale customers. Drive workload characterization, profiling, benchmarking, regression analysis, tooling validation, and optimization across CUDA, NCCL, drivers, firmware, and distributed ML infrastructure. Translate customer performance gaps into engineering priorities, influence multiple teams, and ensure performance validation is ready for deployments and acceptance testing. Top Skills: CudaDcgmDeepspeedDgxFsdpGpu BlastGpu BurnHgxMegatron-LmNcclNvidia Nsight ComputeNvidia Nsight SystemsNvlinkPandasPythonSglangStreamTensorrt-LlmVllm Akamai Technologies, In-Office or Remote United States 169K-305K Annually Expert/Leader 169K-305K Annually Expert/Leader Cloud * Security * Software * Cybersecurity Owns VM and fleet test strategy, performance quality gates, and hypervisor validation for an IaaS platform. Designs reproducible bare-metal-like test environments, develops automated performance benchmarks and regression gates, validates SLOs and deployment safety, and builds test harnesses and analysis pipelines in Go, Python, and Perl. The role requires deep expertise in virtualization, Linux systems, performance engineering, statistical analysis, and cross-architecture debugging. Top Skills: BashCi/CdContainersEbpfGoJenkinsKvmLibvirtLinuxLinux PerfPerlPythonQemuSaltstackVtune, Microsoft, In-Office or Remote United States 120K-304K Annually Senior level 120K-304K Annually Senior level Software * Quantum Computing * Metaverse * Infrastructure as a Service (IaaS) Develop and optimize GPU kernels, compilers, runtimes, and distributed inference systems for large language model serving. Improve latency, throughput, reliability, and hardware efficiency through profiling and low-level performance optimization. Integrate scalable improvements into production systems, collaborate across model, infrastructure, compiler, and hardware teams, and mentor engineers. Principal-level contributors additionally lead cross-stack initiatives and create reusable performance capabilities across models and hardware generations. Top Skills: Amd GpusC++CompilersDistributed Inference SystemsGpu KernelsGpu ProgrammingLlm InferenceNvidia GpusPythonRuntimesSglangVllm

What you need to know about the Colorado Tech Scene

With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.

Key Facts About Colorado Tech

• Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
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• Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
• Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
• Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute