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The Artificial Intelligence Performance team at Microsoft develops AI software that enables running AI models everywhere, from world’s fastest AI supercomputers, to servers, desktops, mobile phones, IoT devices and internet browsers. We collaborate with our hardware teams and partners, both internal and external, and operate at the intersection of AI algorithmic innovation, purpose-built AI hardware, systems, and software. We are a team of highly capable and motivated people that pride themselves on a collaborative and inclusive culture. We own inference performance of OpenAI and other state of the art LLM models and work directly with OpenAI on the models hosted on the Azure OpenAI service serving some of the largest workloads on the planet with trillions of inferences per day in major Microsoft products, including Office, Windows, Bing, SQL Server, and Dynamics. As a Software Engineer on the team, you will have the opportunity to work on multiple levels of the AI software stack, including the fundamental abstractions, programming models, compilers, runtimes, libraries and APIs to enable large scale training and inferencing of models. You will benchmark OpenAI and other LLM models for performance on GPUs and Microsoft HW, debug and optimize performance, monitor performance and enable these models to be deployed in the shortest amount of time and the least amount of HW possible helping achieve Microsoft Azure's capex goals. This is a hands-on technical role requiring software design and development skills. We’re looking for someone who has a demonstrated history of solving technical problems and is motivated to tackle the hardest problems in building a full end-to-end AI stack. An entrepreneurial approach and ability to take initiative and move fast are essential.
Job Responsibility:
Identify and drive improvements to end-to-end inference performance of OpenAI and other state-of-the-art LLMs
Measure, benchmark performance on Nvidia/AMD GPUs and first party Microsoft silicon
Optimize and monitor performance of LLMs and build SW tooling to enable insights into performance opportunities ranging from the model level to the systems and silicon level to improve customer experience and reduce the footprint of the computing fleet
Enable fast time to market of LLMs/models and their deployments at scale by building SW tools that afford velocity in porting models on new Nvidia and AMD GPUs
Design, implement, and test functions or components for our AI/DNN/LLM frameworks and tools
Speeding up/reducing complexity of key components/pipelines to improve performance and/or efficiency of our systems
Communicate and collaborate with our partners both internal and external
Embody Microsoft's Culture and Values
Requirements:
Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, or Python OR equivalent experience
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role
Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter
Nice to have:
Master's Degree in Computer Science or related technical field AND 3+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, or Python OR Bachelor's Degree in Computer Science or related technical field AND 5+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
Technical background and solid foundation in software engineering principles, computer architecture, GPU architecture, HW neural net acceleration
Experience in end-to-end performance analysis and optimization of state of the art LLMs, including proficiency using GPU profiling tools
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
Cross-team collaboration skills and the desire to collaborate in a team of researchers and developers