Microsoft
Senior and/or Principal Software Engineer- AI Frameworks
$120K–$304K
United States · On-site · Principal · Full-time · Posted Nov 2, 2026
- Experience
- 4+ years of technical engineering experience coding; equivalent experience accepted.
- Education
- Bachelor’s Degree in Computer Science or a related technical field, or equivalent experience.
About the job
Build and lead software, performance systems, and engineering tools that make AI models run reliably and efficiently at cloud scale.
What you'll do
- Design, implement, test, and operate AI framework, runtime, benchmarking, and performance-tooling components; optimize model training and inference workloads.
- Build automation and observability, investigate cross-stack performance and reliability issues, and deliver measurable production improvements.
- For principal-level work, set technical direction and lead cross-team initiatives across frameworks, compilers, runtimes, services, and hardware.
What they ask for
- Bachelor’s Degree in Computer Science or a related technical field and 4+ years of technical engineering experience coding in languages such as C++, or Python, or equivalent experience.
- Ability to meet Microsoft, customer and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.
Nice to have
- Experience building and operating complex software systems; performance analysis, benchmarking, automation, or developer tooling.
- Familiarity with AI/ML frameworks, GPU software and profiling technologies, runtimes, collective operations, or networking concepts; principal candidates additionally have architecture, leadership, or platform experience.
Pay
USD $142,800 - $274,800 per year (base salary); USD $188,000 - $304,200 per year (base salary); USD $119,800 - $234,700 per year (base salary); USD $160,200 - $261,000 per year (base salary)
Tools
C++, Python, PyTorch, TensorFlow, ONNX Runtime, CUDA, ROCm, Triton
AI focus
AI framework and systems engineering, including benchmarking and optimizing large language model training and inference across GPUs and Microsoft hardware.
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