Microsoft
Senior Software Engineer, Foundry IQ - CoreAI
$120K–$261K
Redmond, WA +1 · On-site · Senior · Full-time · Posted Oct 14, 2026
- Location
- Redmond, WA
Mountain View, CA - Experience
- 4+ years technical engineering experience; alternatively, equivalent experience. Preferred: 2+ years developing and deploying live production systems as part of a product team.
- Education
- Bachelor's Degree in Computer Science or related technical field, or equivalent experience.
About the job
Build and operate scalable Azure services that bring AI-powered knowledge retrieval and machine learning capabilities into reliable, secure, low-latency production systems.
What you'll do
- Design, build, and maintain Azure backend services and APIs, including integrations with language models and GPUs.
- Partner with Applied Science to productionize prompts, models, and processing components, and provide evaluation tooling for end-to-end AI quality.
- Advance knowledge retrieval and AI integration, including RAG, agentic retrieval, and LLM context engineering; work with customers and partner teams on retrieval challenges.
What they ask for
- Bachelor's Degree in Computer Science or related technical field and 4+ years of technical engineering experience coding in languages including C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
- Ability to meet Microsoft, customer and/or government security screening requirements, including the Microsoft Cloud Background Check upon hire/transfer and every two years.
Nice to have
- Development of Azure Services with service release and live-site responsibilities; production coding in C# and Python.
- Experience developing large language models or GPU-based models and model hosting; 2+ years developing and deploying live production systems as part of a product team.
Pay
USD $119,800 - $234,700 per year (base salary); USD $160,200 - $261,000 per year (base salary)
Tools
Azure, C, C++, C#, Java, JavaScript, Python, GPU
AI focus
Productionize language models and GPU-backed machine learning workloads; improve AI-powered knowledge retrieval through RAG, agentic retrieval, LLM context engineering, and evaluation of end-to-end AI quality.
Model-written summary · the original posting is authoritative