Microsoft AI Hardware Architect 

United States, Washington 

855325956

Strategic Planning and Architecture

This role is for a highly motivated AI Hardware Architect with a solid background in neural networks and hardware. You will be involved with both model development, data type analysis,

Required Qualifications

• 5+ years of technical engineering experience

  • OR Bachelor's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 2+ years of technical engineering experience

  • OR Master's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field.

• 1+ 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:

• in Machine learning, ComputerArchitecture/Systems,Electrical Engineering,High-PerformanceComputingor related areas.
• Understanding of fundamentals of AI workloads/applications.
• Knowledge of commonly used AI and HPC workloads and how they are mapped onto large scale systems.
• Hands on experiencewithframeworks such asPytorch/Tensorflow/TensorRT.
• Deep knowledge ofCNN/transformer architecture andoptimizationstrategies– 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 August 28, 2024.

• Analyse Hardware Architecture for AI workloads.
• Architecting large scale systems which support breakthrough performance AI workloads to shape.
• Azure’s AIinfrastructureroadmap.
• Benchmark GPUs and other AI hardware IPs.
• DriveNNmodel/HW codesign.
• Understand business critical AI workloads/applications.
• Developing andanalysingnovel NN architectures.
• Embody our