Senior Software Engineer, Performance

Microsoft

Quick summary

Work type
On-site
Location
Mountain View, CA
Salary
$119,800–$234,700 / yr
Posted
19 days ago
Closes
Dec 5, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $192k
This role $177k
$106k most similar roles pay here $248k

This role pays more than 52% of similar roles. Most pay $166,100–$217,725 — the shaded band above. At the midpoint, this role pays about $177k versus about $192k for comparable roles.

Based on 240 similar postings.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 622 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 571 roles with salary data.

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At a glance

TL;DR · Senior Software Engineer, Performance

As a Senior Software Engineer on Microsoft’s Artificial Intelligence Performance team, you will work on optimizing the inference performance of state-of-the-art large language models (LLMs) like those from OpenAI across various hardware platforms, including GPUs and custom silicon. Your day-to-day responsibilities include benchmarking model performance, identifying bottlenecks, and developing software tools to enhance deployment efficiency. You’ll collaborate closely with internal teams and external partners to ensure rapid time-to-market for new LLMs while minimizing computational resource requirements. The role demands expertise in GPU architecture, deep learning frameworks like PyTorch or TensorFlow, and proficiency with CUDA and ROCm. This position is integral to Microsoft’s mission of enabling seamless AI deployment across diverse devices, from supercomputers to mobile phones, supporting major products such as Office, Windows, Bing, and Dynamics.

What you'll do

  • Identify and drive improvements for end-to-end inference performance of LLMs.
  • Benchmark performance on Nvidia/AMD GPUs and Microsoft hardware.
  • Optimize and monitor performance of large language models across various levels.
  • Build software tools to enable fast deployment of LLMs on new GPUs.
  • Design, implement, and test components for AI/DNN/LLM frameworks and tools.
  • Reduce complexity in key system components to enhance overall efficiency.

What we're looking for

  • Experience in end-to-end performance analysis and optimization of state-of-the-art LLMs.
  • Proficiency using GPU profiling tools for benchmarking and optimizing AI models.
  • Technical background in software engineering, computer architecture, and GPU acceleration.
  • Expertise in DNN/LLM inference with proficiency in DL frameworks like PyTorch or TensorFlow.
  • Ability to design, implement, and test functions for AI/DNN/LLM frameworks and tools.
  • Strong cross-team collaboration skills and experience working with researchers and developers.

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