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.
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