Senior and/or Principal Software Engineer- AI Infrastructure

Microsoft•,

2d•$142,800 - $331,200

About The Position

The Microsoft AI Frameworks team develops software, performance systems, and engineering tools to enable state-of-the-art AI models to run reliably and efficiently at cloud scale. This role involves working across model architectures, frameworks, compilers, runtimes, libraries, observability, benchmarking, and hardware platforms. The Senior and/or Principal Software Engineers - AI Frameworks will 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 translate 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, combining strong software engineering fundamentals with curiosity about AI workloads, disciplined measurement, and a willingness to work across organizational boundaries to deliver production impact. Microsoft's mission is to empower every person and every organization on the planet to achieve more, guiding how they partner with customers to deliver trusted, impactful solutions. With a growth mindset culture, they innovate responsibly and measure success by shared progress across people, teams, and customers.

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.
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
Demonstrated cross-team collaboration and technical ownership
Extensive experience designing and shipping complex, high-performance or distributed software systems
Experience 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 that 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.
Embody Microsoft’s culture and values.
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 senior engineers, developing technical leaders, and advancing standards for quality, maintainability, and inclusive collaboration.