Microsoft
Senior Software Engineer
$120K–$261K
Redmond, WA · On-site · Senior · Full-time · Posted Nov 3, 2026
- Experience
- 4+ years technical engineering experience with coding; equivalent experience accepted. Preferred alternatives: Master's Degree and 6+ years, or Bachelor's Degree and 8+ years, or equivalent experience.
- Education
- Bachelor's Degree in Computer Science or related technical field, or equivalent experience.
- Visa sponsorship
- The role requires Microsoft, customer and/or government security screening, including the Microsoft Cloud Background Check.
About the job
Leads development of the global runtime, deployment, orchestration, and release infrastructure for Azure Managed Redis, including applying AI to improve cloud operations and engineering workflows.
What you'll do
- Lead architecture and build systems to deploy, upgrade, orchestrate, and safely release Redis clusters across Azure regions.
- Integrate Redis Enterprise software into Azure Managed Redis while maintaining availability, data consistency, performance, and reliability.
- Use AI techniques to detect, diagnose, and respond to fleet issues and improve DevOps and engineering workflows.
What they ask for
- Bachelor's Degree in Computer Science or related technical field and 4+ years technical engineering experience coding, or equivalent experience.
- Ability to meet Microsoft, customer and/or government security screening requirements, including the Microsoft Cloud Background Check.
- Pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Nice to have
- 2+ years building and operating distributed, highly scalable online services on Kubernetes in Azure or a similar cloud platform.
- Experience with AI-assisted development tools, large language models, or automation to improve engineering workflows or operations.
Pay
USD $119,800 - $234,700 per year (base salary); USD $160,200 - $261,000 per year (base salary)
Tools
C#, Go, C, C++, JavaScript, Python, Kubernetes, Azure
AI focus
Apply GitHub Copilot, LLMs, and anomaly-detection models to enhance DevOps, operations, and engineering workflows, including detecting, diagnosing, and responding to fleet issues.
Model-written summary · the original posting is authoritative