Senior and/or Principal Software Engineer- AI Frameworks

Microsoft•,

2d•$119,800 - $304,200

About The Position

The Microsoft AI Frameworks team develops software, performance systems, and engineering tools that enable state-of-the-art AI models to run reliably and efficiently at cloud scale. Its work spans model architectures, frameworks, compilers, algorithms, runtimes, libraries, observability, benchmarking, and hardware platforms, including NVIDIA and AMD GPUs and Microsoft silicon. 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 involving ambiguous, end-to-end systems problems. Senior Software Engineers own significant components and projects across AI performance, benchmarking, automation, and developer tooling. They translate model and platform needs into robust software, investigate complex performance and reliability issues, and work with partner teams to deliver measurable production improvements. Principal Software Engineers set technical direction and lead high-impact initiatives spanning AI frameworks, performance engineering, benchmarking, and tooling. They identify systemic opportunities, align teams around durable architectures and success measures, and remain hands-on in delivering solutions that improve model velocity, platform efficiency, and production reliability.

Requirements

Bachelor’s degree in Computer Science or a related technical field and 4+ 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, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

Nice To Haves

Experience building and operating complex software systems.
Practical knowledge of performance analysis, benchmarking, automation, or developer tooling.
Familiarity with AI/ML frameworks such as PyTorch, TensorFlow, or ONNX Runtime.
Familiarity with GPU software and profiling technologies such as CUDA, ROCm, Triton, or equivalent.
Familiarity with runtimes, collective operations, and networking concepts.
Demonstrated cross-team collaboration and technical ownership.
Extensive experience designing and shipping complex, high-performance, or distributed software systems.
Foundation in software architecture, computer architecture, and accelerator-aware optimization.
Experience with AI/ML workloads and frameworks.
Demonstrated leadership of cross-team technical initiatives from strategy through production.
Track record of creating reusable platforms, influencing senior stakeholders, and mentoring technical leaders.

Responsibilities

Design, implement, test, and operate production-quality components across AI frameworks, runtimes, benchmarking systems, performance tooling, and service integrations.
Benchmark, profile, debug, and optimize large language model training and inference workloads across GPUs and Microsoft hardware.
Build automation and observability to detect regressions, improve reproducibility, surface actionable insights, and accelerate model and hardware onboarding.
Drive scoped projects from problem definition through deployment, balancing delivery speed, maintainability, reliability, and measurable customer or capacity impact.
Partner with researchers, model teams, infrastructure owners, and hardware vendors to diagnose cross-stack issues and deliver production-ready solutions.
Contribute to technical design reviews, engineering standards, operational health, and mentoring of other engineers.
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, aligning researchers, product groups, infrastructure owners, and hardware partners around decisions and execution plans.
Provide hands-on technical leadership through prototypes, critical-path implementation, design and code reviews, complex debugging, and operational readiness.
Mentor senior engineers, develop technical leaders, and advance standards for quality, maintainability, and inclusive collaboration.
Embody Microsoft’s culture and values.