The Senior Software Engineer - Performance Tooling role at Microsoft involves working on the AI Frameworks team, which develops AI software for various platforms. The team focuses on optimizing performance for large language models and collaborating with hardware teams to enhance AI capabilities. This position requires expertise in software engineering principles and performance optimization on GPUs and CPUs. The engineer will benchmark models, debug performance issues, and drive improvements to achieve efficiency in deployment.

• Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C++, or Python OR equivalent experience.
• Ability to meet Microsoft, customer and/or government security screening requirements.
• Master's Degree in Computer Science or related technical field AND 6+ 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 8+ years technical engineering experience with coding in languages including, but not limited to, C++, or Python OR equivalent experience.
• 4+ years’ practical experience working on high performance applications and performance debugging and optimization on CPUs/GPUs.
• Experience in DNN/LLM inference and experience in one or more DL frameworks such as PyTorch, Tensorflow, or ONNX Runtime and familiarity with CUDA, ROCm, Triton.
• Technical background and solid foundation in software engineering principles, computer architecture, GPU architecture, hardware neural net acceleration.
• Experience in end-to-end performance analysis and optimization of state of the art LLMs and HPC applications, including proficiency using GPU profiling tools.
• Cross-team collaboration skills and the desire to collaborate in a team of researchers and developers.
• Ability to independently lead projects.

• Work across multiple layers of the AI software stack to enable large-scale model training and inference.
• Benchmark OpenAI and other LLMs for performance on GPUs and Microsoft hardware.
• Debug, profile, and optimize performance for training/inference workloads on CPUs/GPUs.
• Monitor performance regressions and drive continuous improvements to reduce time-to-deploy and hardware footprint.
• Collaborate across teams of researchers and engineers to deliver scalable, production-ready AI performance improvements.

• Employees at Microsoft are often offered comprehensive, “world-class” benefits—including health and mental-wellness programs, competitive pay with bonuses and stock awards, and retirement/savings options. Time-off and flexibility are common, with generous vacation and holidays, parental and caregiver leave, and flexible work schedules, alongside learning support, employee resource groups, product discounts, and matching-gifts/volunteering programs. Specific benefits can vary by region.