Principal Software Engineer- AI Frameworks

Microsoft•,

2d•$142,800 - $304,200

About The Position

The Microsoft AI Frameworks team develops the software, performance systems, and engineering tools that enable state-of-the-art AI models to run reliably and efficiently at cloud scale. We work across model architectures, frameworks, compilers, runtimes, libraries, observability, benchmarking, and hardware platforms—including NVIDIA and AMD GPUs and Microsoft silicon. Our Principal Software Engineers partner with model developers, researchers, hardware teams, and production services to accelerate model onboarding, improve performance and reliability, reduce deployment time and hardware footprint, and turn performance insights into durable platform capabilities. This is a hands-on individual-contributor role for engineers who enjoy solving ambiguous, end-to-end systems problems. Successful candidates combine strong software engineering fundamentals with curiosity about AI workloads, disciplined measurement, and a willingness to work across organizational boundaries to deliver production impact. As a Principal Software Engineer, you will set technical direction and lead high-impact initiatives spanning AI frameworks, performance engineering, benchmarking, and tooling. You will identify systemic opportunities, align teams around durable architectures and success measures, and remain hands-on in the design and delivery of solutions that improve model velocity, platform efficiency, and production reliability. At Microsoft, our mission to empower every person and every organization on the planet to achieve more guides how we partner with customers to deliver trusted, impactful solutions. With a growth mindset culture, we innovate responsibly and measure success by shared progress people, teams, and customers. Join us to do meaningful work that changes the world and helps shape what’s next for everyone.

Requirements

Bachelor’s Degree in Computer Science or a related technical field and 6+ 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.
This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

Deep expertise in GPU or equivalent accelerator programming, compilation, and low-level execution, including intermediate representations, lowering, code generation, instruction-level behavior, memory hierarchy, and synchronization.
Expertise in parallelism strategies used in LLM training and inference, including tensor, pipeline, data, and expert parallelism, with the ability to evaluate their suitability for different models and hardware configurations.
Expertise in distributed inference acceleration, including prefill/decode disaggregation, KV-cache transfer, collective communication, and compute/communication overlap.
Strong understanding of LLM serving architectures such as vLLM, SGLang, or equivalent systems, with a track record of translating architectural improvements into measurable production gains.
Demonstrated leadership of cross-team technical initiatives from strategy and design through implementation, deployment, and sustained production impact.
A track record of creating reusable platforms, influencing stakeholders, and mentoring engineers.
Ability to use AI-assisted development tools effectively and establish practices that improve engineering productivity without compromising correctness, performance, or maintainability.

Responsibilities

Define technical vision, architecture, and multi-release strategy for critical AI framework, performance, benchmarking, or developer-productivity capabilities.
Lead ambiguous, cross-stack investigations and investments spanning models, frameworks, compilers, runtimes, systems, services, and silicon.
Establish 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.
Influence architecture and priorities across teams; align researchers, product groups, infrastructure owners, and hardware partners around clear decisions and execution plans.
Provide hands-on technical leadership through prototypes, critical-path implementation, design and code reviews, complex debugging, and operational readiness.
Raise the engineering bar by mentoring junior engineers, developing technical leaders, and advancing standards for quality, maintainability, and inclusive collaboration.
Embody Microsoft’s culture and values.