Microsoft AI System Architect 

United States, Washington 

794595587

This role is for a highly motivated AI System Architect with experience defining and modeling system performance while working within system, rack, and data center constraints. A qualified candidate will have a good understanding of AI workload mappings across large distributed systems. In this role you will help drive system architecture for a variety of AI products including inferencing and training optimized systems.

Requierd 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

  o OR equivalent experience.

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

• Experience and background in networking technologies – transports, switch functionality, optical networking, SI analysis
• Experience and background in system architecture, physical system layout
• Experience driving TCO and performance modeling based tradeoff analysis
• Experience with system modeling, physical design, mechanical engineering
• Past experience with high-performance networking, including working with NVLink, InfiniBand, Ethernet, or similar, especially as it related to physical implementations considerations
• Growth mindset/curiosity for learning and deploying new technologies.

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 10, 2024.

• Participate in architecture definition and modeling for AI systems
• Leverage Total Cost of Ownership (TCO) and Performance models to define and refine system architecture tradeoffs
• Track and define system roadmaps including upcoming technology intercepts and inflection points
• Take a whole-stack view to identify and pursue server, rack, and DC-level optimization opportunities for AI workloads.

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