Principal Software Engineering Manager - Substrate efficiency

Microsoft

Quick summary

Work type
On-site
Location
Redmond, WA
Salary
$165,600–$296,400 / yr
Posted
3 days ago
Closes
Dec 21, 2026

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $200k
This role $231k
$126k most similar roles pay here $315k

This role pays more than 82% of similar roles. Most pay $175,300–$223,700 — the shaded band above. At the midpoint, this role pays about $231k versus about $200k for comparable roles.

Based on 239 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 559 roles with salary data.

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At a glance

TL;DR · Principal Software Engineering Manager - Substrate efficiency

As a Principal Software Engineering Manager at Microsoft’s M365 Copilot inference team, you will lead a strategic initiative to enhance the efficiency of AI inference platforms by optimizing model execution and runtime performance. Your day-to-day responsibilities include building and leading a high-performing engineering team focused on improving throughput per GPU, reducing cost per query, and enhancing live-site performance reliability. You will collaborate with cross-functional teams such as M365 Core, AI Core, Azure, and Microsoft Research to co-design and implement advanced optimizations, establish metrics for efficiency gains, and drive alignment across partner teams on optimization priorities. The ideal candidate has extensive experience in leading engineering teams building backend or distributed systems, hands-on expertise in improving system throughput and resource utilization, and familiarity with GPU-based workloads and AI/ML inference runtime optimization techniques. This role operates at massive scale, pushing the boundaries of performance and efficiency in one of the world’s largest AI inference platforms.

What you'll do

  • Build and lead a high-performing engineering team focused on optimizing model execution performance.
  • Define and execute strategies to enhance throughput per GPU through advanced runtime optimizations.
  • Increase agility for faster experimentation, iteration, and rollout of performance improvements.
  • Partner with cross-functional teams to co-design and implement advanced inference optimizations.
  • Establish metrics and frameworks to measure efficiency gains and guide investment decisions.
  • Ensure live-site performance, reliability, and operational excellence for large-scale inference engines.

What we're looking for

  • Extensive experience leading engineering teams focused on backend or distributed systems.
  • Proven track record of optimizing system performance and resource utilization at scale.
  • Strong background in driving system-level improvements for workload execution, scheduling, and batching.
  • Ability to translate technical insights into clear engineering priorities and execution plans effectively.
  • Comfortable collaborating across multiple teams to align on goals and drive execution.
  • Hands-on experience with AI/ML inference systems or GPU-based workloads.
  • Familiarity with techniques for optimizing inference runtime performance and efficiency.

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