Principal Software Engineer

Microsoft

Hybrid

Quick summary

Work type
Hybrid
Location
Salary
$142,800–$274,800 / yr
Posted
88 days ago
Closes
Sep 27, 2026

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $177k
This role $209k
$124k most similar roles pay here $291k

This role pays more than 79% of similar roles. Most pay $145,343–$208,800 — the shaded band above. At the midpoint, this role pays about $209k versus about $177k 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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View all roles at Microsoft

At a glance

TL;DR · Principal Software Engineer

As a Principal Software Engineer on the Ads Engineering Platform team at Microsoft Advertising, you will lead the design and optimization of high-performance ad-serving systems that process millions of requests per second with ultra-low latency. Your responsibilities include architecting and optimizing end-to-end inference infrastructure for deep learning models and large-scale distributed orchestration across GPU and CPU tiers. You will also profile and optimize performance across the full stack, from CUDA kernels to OS-level scheduling, ensuring cost efficiency and reliability in a globally distributed system. This role requires expertise in NVIDIA Triton Inference Server, CUDA, TensorRT, and real-time data streaming systems like Kafka and Flink, as well as deep knowledge of GPU inference frameworks and low-level system internals for large-scale serving pipelines.

What you'll do

  • Design and lead development of high-performance ad-serving systems processing millions of requests per second.
  • Optimize end-to-end inference infrastructure for GPU-accelerated and CPU-based ranking/pipelines across heterogeneous hardware.
  • Profile and optimize system performance from CUDA kernels to OS-level scheduling, identifying and mitigating bottlenecks.
  • Own live-site reliability as DRI, designing telemetry, alerting, and fault-tolerance mechanisms for globally distributed systems.
  • Mentor and drive architecture reviews, promoting engineering excellence in deep debugging, profiling, and performance optimization.

What we're looking for

  • Extensive experience in designing and optimizing high-performance, distributed online serving systems.
  • Hands-on expertise with real-time data streaming systems and multi-region deployment for low-latency services.
  • Deep knowledge of GPU inference frameworks including NVIDIA Triton Inference Server, CUDA, and TensorRT.
  • Proven ability to profile and optimize system workloads across CPU and GPU tiers.
  • Solid understanding of model-serving trade-offs and optimization techniques for deep learning and LLM workloads.
  • Expertise in low-level system internals such as multi-threading, NUMA-aware memory allocation, and I/O stack tuning.

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