Principal AI Optimization Software Engineer Lead

The AI Frameworks Group builds the software foundation that enables Microsoft to deploy and run AI models efficiently across a rapidly evolving set of hardware platforms. Our mission is to ensure that every relevant AI model runs as efficiently as possible on every Microsoft AI accelerator. Because AI powers nearly every Microsoft product and service, our work has a direct impact on the performance, scalability, and profitability of the entire company. At Microsoft’s scale, even modest improvements in efficiency can have an outsized impact on infrastructure costs, making this one of the highest-impact engineering opportunities in the company.  We are looking for AI Optimization experts who are passionate about pushing the limits of AI performance through ML algorithm innovation, advanced AI compilation and hardware optimization, runtime and serving optimizations, and innovations in distributed execution.  You will work across the AI software stack, partnering with model, systems, compiler, and hardware teams to deliver breakthroughs in performance, scalability, and efficiency for Microsoft’s AI workloads across every hardware platform.

Coding Provides technical leadership during code reviews for a solution/product area to assure it meets team standards, contains the correct test coverage, and is appropriate for the product or solution area. Brings expertise to code reviews to help improve code quality, proactively coaching and providing feedback to develop other engineers' skills. Ensures coding standards are followed. Screens for and establishes best practices in reviews and provides feedback on code to drive adherence to best practices. Uses automated source code analysis tools that are incorporated into the build/development process. Leads by example across teams and mentors others to produce extensible, maintainable, well-tested, secure, and performant code used across the company that adheres to design specifications. Leads efforts to continuously improve code performance, testability, maintainability, effectiveness, and cost, while accounting for and incorporating relevant trade-offs. Identifies best practices and coding patterns (e.g., leveraging state-of-the-art generative artificial intelligence [GenAI], approaches to source code organization, naming conventions) and provides deep expertise in the coding and validation strategy. Creates and applies metrics to drive code quality and stability, appropriate coding patterns, and best practices. Leads efforts to identify and anticipate blockers or unknowns during the development process, escalate them, and communicate how they will impact timelines, and then drives the identification and implementation of strategies and/or opportunities to address them. Acts as an expert on using debugging tools, tests, logs, telemetry, and other methods, and proactively leads verification of assumptions through while developing code before issues occur across products and teams in production. Leverages minimal telemetry data, triangulates issues, and resolves with minimal iterations. Leads incident retrospectives to identify root causes of problems, and owns the implementation of repair actions and the identification of mechanisms to prevent incident recurrence. Drives applying least-access principles, using logging, telemetry, and other appropriate mechanisms to investigate issues while retaining privacy and security, and champions those practices across the team. Design Establishes best practices and mentors others to create a clear test strategy that ensures solution quality, prevents regression from being introduced into existing code. Establishes best practices and mentors others on ensuring test plans incorporate security testing to validate security invariants (including negative cases). Provides technical leadership on adding new tests to cover gaps, deleting or fixing broken tests, and improving the speed, reliability, and defect localization of