below marketmost similar roles pay hereabove market
This role pays more than
57%
of similar roles. Most pay
$174,600–$241,550
— the blue band above.
At the midpoint, this role pays about
$209k
versus about
$208k
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
634 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 612 roles with salary data.
The Principal Software Engineer - AI Infrastructure will join the team to advance the reliability and observability architecture of a large-scale, globally distributed AI platform. This role involves defining the technical roadmap for platform health, designing unified telemetry capabilities, and developing safeguards for overload protection, capacity management, and routing integrity. The engineer will establish practices for production validation, fault testing, and automated rollbacks while leading cross-team architecture efforts and mentoring other engineers. Required skills include proficiency in coding languages such as C, C++, C#, Java, JavaScript, or Python. The work focuses on solving complex problems in distributed systems, cloud infrastructure, and AI inference, specifically addressing high-performance computing, accelerator-based infrastructure, and request tracing across distributed services to ensure operational health and reliability.
What does a Software Engineer earn?
Median $196750 from 2546 postings across 156 companies.
Define and drive the architecture and technical roadmap for platform reliability, observability, and operational health.
Design unified health and telemetry capabilities connecting customer impact with infrastructure and deployment signals.
Develop platform safeguards for overload protection, capacity management, routing integrity, and automated recovery.
Establish engineering practices for production validation, progressive delivery, fault testing, and automated rollback.
Advance end-to-end request tracing and diagnostics across distributed services and asynchronous operations.
Lead cross-team architecture efforts and mentor engineers to turn production learnings into reusable platform capabilities.
What we're looking for
Bachelor's Degree in Computer Science or related technical field and 6+ years of technical engineering experience with coding in languages like C, C++, C#, Java, JavaScript, or Python.
Equivalent experience to the Bachelor's Degree and 6+ years of technical engineering experience is accepted.
Master's Degree in Computer Science or related technical field and 8+ years of technical engineering experience with coding in languages like C, C++, C#, Java, JavaScript, or Python (preferred).
Bachelor's Degree in Computer Science or related technical field and 12+ years of technical engineering experience with coding in languages like C, C++, C#, Java, JavaScript, or Python (preferred).
Experience with AI/ML serving platforms, high-performance computing, accelerator-based infrastructure, or other compute-intensive distributed systems (preferred).
Experience with distributed tracing, capacity management, load balancing, admission control, retries, backpressure, and graceful degradation (preferred).
Experience designing, building, and operating large-scale distributed systems, cloud services, or other complex production platforms (preferred).
Experience with reliability engineering, observability, service health, telemetry, and production incident response (preferred).
Azure AI
Python
C++
C#
Java
JavaScript
C
Azure OpenAI
Azure ML
Azure AI Studio
CI/CD
DevOps
Distributed Systems
Machine Learning
SDLC
Observability
Telemetry
Kubernetes
Go
Python
Linux
AWS
EKS
Terraform
Argo CD
Helm
Prometheus
Grafana
PyTorch
Ray
vLLM
SGLang
TensorRT-LLM
CUDA
NVLink
NVSwitch
NCCL
EFA
DCGM
Containers
HPC
ROCm
CUDA
OpenCL
GPU Architecture
CPU Architecture
Hardware/Software Co-design
Compilers
Kernels
Runtime
AI/ML Frameworks
Performance Optimization
LLMs
MoE
Device Driver Development
Operating Systems
Open Source