Drimva

Microsoft

Principal Software Engineer - Systems and Infrastructure

$166K–$331K

Mountain View, CA · On-site · Principal · Full-time · Posted Oct 13, 2026

Experience
8+ years technical engineering experience; preferred alternatives are 12+ years with a Master's Degree, or 15+ years with a Bachelor's Degree, or equivalent experience.
Education
Bachelor's Degree in Computer Science or related technical field, or equivalent experience.

About the job

Provides technical leadership and owns architecture for large-scale infrastructure platforms that support Microsoft products and AI services, from design through production operations.

What you'll do

  • Lead architecture and delivery across complex, large-scale infrastructure initiatives, balancing customer, business, and platform needs.
  • Build reusable, secure, reliable, scalable platform services and establish engineering standards; mentor engineers and technical leaders.
  • Improve the performance, efficiency, and reliability of large-scale AI workloads, including model inference, and lead operational debugging and incident prevention.

What they ask for

  • Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding, OR equivalent experience.
  • Coding experience in one or more of C, C++, C#, Java, JavaScript, or Python.
  • Ability to meet Microsoft, customer and/or government security screening requirements, including the Microsoft Cloud Background Check.

Nice to have

  • Master's Degree and 12+ years of technical engineering experience; OR Bachelor's Degree and 15+ years; OR equivalent experience.
  • Experience with large-scale distributed systems or platforms, technical leadership, systems languages and concepts, open-source projects, or AI/ML infrastructure.

Pay

USD $165,600 - $296,400 per year (base salary); USD $220,800 - $331,200 per year (base salary)

Tools

C, C++, C#, Java, JavaScript, Python, Rust

AI focus

Infrastructure for AI services and workloads, including improving model inference performance, efficiency, and reliability; preferred familiarity includes model serving, GPU computing, and distributed training.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 5, 2026 (UTC)