Optimizing AI Frameworks for Global Scalability

7 days ago

Mountain View, California, United States Microsoft Full time

The AI Frameworks team at Microsoft is responsible for developing cutting-edge software that enables the execution of AI models on a wide range of devices, from powerful supercomputers to servers, desktops, mobile phones, IoT devices, and web browsers. Collaboration with hardware teams and partners allows us to build tailored software stacks for novel AI accelerators.

By working closely with machine learning researchers and developers, we optimize and scale out model training and inference processes. Our team operates at the intersection of AI algorithmic innovation, purpose-built AI hardware, systems, and software.

We own the inference performance of OpenAI and other state-of-the-art Large Language Models (LLMs), working directly with OpenAI to host these models on the Azure OpenAI service, which serves massive workloads in major Microsoft products like Office, Windows, Bing, SQL Server, and Dynamics.

This role involves working on multiple levels of the AI software stack, including fundamental abstractions, programming models, compilers, runtimes, libraries, and APIs to facilitate large-scale model training and inference. You will benchmark OpenAI and other LLMs for performance on GPUs and Microsoft hardware, debug and optimize performance, monitor performance, and enable model deployment within the shortest timeframe and least amount of hardware possible, contributing to Microsoft Azure's capital expenditure goals.

This is a technical position that requires hands-on software design and development skills. We're seeking someone with a proven track record of solving complex technical problems and a motivation to tackle challenging tasks in building a comprehensive end-to-end AI stack.

Key Responsibilities:

  1. Drive improvements to end-to-end inference performance of OpenAI and other state-of-the-art LLMs.
  2. Benchmark performance on Nvidia/AMD GPUs and first-party Microsoft silicon.
  3. Optimize and monitor LLM performance, develop SW tooling for performance insights, and reduce computing fleet footprint to achieve Azure AI capex goals.
  4. Enable fast time-to-market for LLMs/models by building SW tools for velocity in porting models on new Nvidia, AMD GPUs, and Maia silicon.
  5. Design, implement, and test functions/components for our AI/DNN/LLM frameworks and tools.
  6. Streamline key components/pipelines to improve performance/effectiveness of our systems.
  7. Collaborate with internal and external partners.

Requirements:

  1. Bachelor's Degree in Computer Science or related field AND 4+ years of technical engineering experience with coding in languages such as C, C++, or Python OR equivalent experience.
  2. 2+ years' practical experience working on High-Performance Applications and Performance Debug/Optimization on CPUs/GPUs.

Preferred Qualifications:

  1. Master's Degree in Computer Science or related field AND 6+ years of technical engineering experience with coding in languages such as C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related field AND 8+ years of technical engineering experience with coding in languages such as C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
  2. Technical background and solid foundation in software engineering principles, computer architecture, GPU architecture, HW neural net acceleration.
  3. Experience in end-to-end performance analysis and optimization of state-of-the-art LLMs, HPC applications, including proficiency using GPU profiling tools.
  4. Experience in DNN/LLM inference and familiarity with one or more DL frameworks such as PyTorch, Tensorflow, or ONNX Runtime and knowledge of CUDA, ROCm, Triton.
  5. Cross-team collaboration skills and a willingness to collaborate with researchers and developers.
  6. Experience in working with orchestration platforms like K8 and Service Fabric.
  7. 2+ years of experience with Deep Learning and AI Infrastructure, including Diagnostic, Profiling, and Performance Analysis Tools.

The estimated salary for this position ranges between $117,200 and $229,200 per year, depending on the specific location. Certain roles may be eligible for additional benefits and compensation. Find more information here: https://careers.microsoft.com/us/en/us-corporate-pay. Microsoft accepts ongoing applications and offers for these roles. The company is an equal opportunity employer and provides a supportive environment for all qualified applicants.

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