Microsoft Principal AI Network Architect 

United States, Washington 

299745814

Required Qualifications:

• Bachelor's Degree in Computer Science, or related technical discipline AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience.
• 5+ years of experience developing/architecting hardware and/or networks.
• Experience with network simulation environments like HTSim and NS3.

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 Qualifications:

• MS/PhD in Machine learning, Computer Architecture/Systems, Electrical Engineering, High-Performance Computing or related areas.
• Understanding of large language models, training and inference.
• Experience in python programming and Software engineering.
• Effective communication and collaborative mindset.
• Good problem-solving skills and attention to detail.
• Working knowledge of prevailingLarge Language Models(LLM) and frameworks like Tensorflow, Pytorch is a plus.

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 January 3, 2025.

• Propose and evaluate system architectures to meet stringent workload and datacenter requirements.
• Maintain and leverage in-house performance modeling tools for AI workload and Networking evaluation.
• Identify performance bottlenecks, optimize resource utilization, and implement improvements to enhance performance.
• Help architect large scale systems which support breakthrough performance AI workloads to shape Azure’s AI infrastructure roadmap.
• Drive Neural Networks(NN) model/Hardware(HW) codesign.
• Understand business critical AI workloads/applications.
• Other
  • Embody our and