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Microsoft Senior AI Hardware Architect 
United States, Washington 
508844037

10.09.2024

Required Qualifications

  • 7+ years of technical engineering experience

    • OR Bachelor's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years of technical engineering experience

    • OR Master's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years of technical engineering experience

    • OR Doctorate degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field.

  • 3+ years of experience in Computer Architecture or AI Systems.

Other Requirements

  • Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check:
    • This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred/Additional Qualifications:

  • MS/PhD in Machine learning, Computer Architecture/Systems, Electrical Engineering, High-PerformanceComputingor related areas.
  • Knowledge of commonly used AI and HPC workloads and how they are mapped onto large scale systems.
  • Hands on experience with frameworks such asPytorch/Tensorflow/TensorRT.
  • Deep knowledge of CNN/transformer architecture and optimization strategies – quantization, sparsity, sharding, KV Cache, Flash Attention.
  • Solid programming skills in Python/C/C++.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

Microsoft will accept applications for the role until September 11, 2024.

Responsibilities
  • Analyse Hardware Architecture for AI workloads.
  • Architecting large scale systems which support breakthrough performance AI workloads to shapeAzure’s AI infrastructure roadmap.
  • Benchmark GPUs and other AI hardware IPs.
  • Drive NN model/HW codesign.
  • Understand business critical AI workloads/applications.
  • Developing and analysing novel NN architectures.
  • Embody our