Microsoft
Senior Software Engineer, AI Infrastructure
$120K–$261K
Redmond, WA · On-site · Senior · Full-time · Posted Oct 16, 2026
- Experience
- Required: 4+ years technical engineering experience building distributed systems, with the stated degree or equivalent experience. Preferred alternatives: Master's degree and 6+ years; Bachelor's degree and 8+ years; or equivalent experience. Additional preferred experience includes 3+ years running distributed services on public cloud platforms and 2+ years with traffic routing/scheduling or computing resource allocation.
- Education
- Bachelor's Degree in Computer Science or related technical field, or equivalent experience.
About the job
Designs and operates global infrastructure services that route and prioritize AI requests and allocate computing capacity for Microsoft AI services.
What you'll do
- Design and build platform components for AI traffic routing and capacity management, balancing speed, reliability, scale, and cost.
- Write and review code; deliver changes through milestones, feature flags, and gradual rollouts.
- Support production reliability through on-call duties, monitoring, troubleshooting, and incident reduction.
What they ask for
- Bachelor's Degree in Computer Science or related technical field and 4+ years technical engineering experience building distributed systems, coding in languages including C#, Java, or Go; OR equivalent experience.
- Ability to meet Microsoft, customer and/or government security screening requirements, including the Microsoft Cloud Background Check.
Nice to have
- Experience with public cloud platforms, traffic routing or scheduling, compute allocation, AI/ML infrastructure, or highly available services.
- Experience with Python, Docker, and Kubernetes; or delivering projects with partner teams.
Pay
USD $119,800 - $234,700 per year (base salary); USD $160,200 - $261,000 per year (base salary)
Tools
C#, Java, Go, Microsoft Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP), Python, Docker
AI focus
Builds and operates infrastructure that routes and prioritizes AI requests, allocates computing capacity, and supports AI/ML model serving; uses AI tools throughout the software development lifecycle.
Model-written summary · the original posting is authoritative