below marketmost similar roles pay hereabove market
This role pays more than
56%
of similar roles. Most pay
$169,550–$245,200
— the blue band above.
At the midpoint, this role pays about
$209k
versus about
$207k
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
694 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 651 roles with salary data.
The Principal Software Engineer, ML & Distributed Systems joins the team to design, build, and operationalize machine learning systems and hyperscale distributed services. This role involves owning systems end to end, from model development and evaluation through serving and orchestration layers to reliable operation at hyperscale. You will develop LLM prompt and fine-tuning strategies, build evaluation pipelines, and architect multi-tiered distributed services with high availability and low latency. Key responsibilities include building model serving infrastructure, managing GPU capacity, and designing scalable APIs and data pipelines. You will utilize technologies including C, C++, C#, Java, JavaScript, Python, Kubernetes, and Redis. The work focuses on powering personalized content feeds through AI-powered personalization, content moderation, and content entry points within a large-scale distributed environment.
What does a Software Engineer earn?
Median $197400 from 2576 postings across 150 companies.
Design, build, and operationalize scalable ML and deep learning models using containers and orchestration platforms.
Develop LLM prompt and fine-tuning strategies while building evaluation pipelines to optimize quality, latency, and cost.
Architect and operate multi-tiered distributed services at hyperscale with high availability and low latency.
Build model serving and inference infrastructure including caching, batching, and GPU capacity management.
Design scalable APIs, data pipelines, and feature stores to ensure secure data flow between ML systems and products.
Drive live-site excellence through instrumentation, monitoring, capacity planning, and incident response for ML-backed services.
Mentor engineers across both ML and systems disciplines and participate in architectural discussions.
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 (or equivalent).
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).
Proven experience designing, developing, and operating multi-tiered distributed services at scale (preferred).
Hands-on experience building, deploying, and operating machine learning systems in production, including model training, evaluation, and LLM-based applications (preferred).
Experience working through full product cycles from initial design to final delivery (preferred).
Experience with LLM application patterns including prompt engineering, RAG, fine-tuning, and model evaluation frameworks (preferred).
Experience with ML infrastructure, large-scale data systems, and orchestration platforms such as Kubernetes (preferred).
Distributed Systems
Vector Index
C++
Python
Java
C#
JavaScript
Search Engine
Approximate-nearest-neighbor search
LLM
Big Data
System Architecture
Performance Optimization
Data Access Patterns
Software Development Lifecycle
Distributed Systems
Data Pipelines
Microservices
APIs
Apache Spark
Kafka
Columnar Storage
Data Modeling
CI/CD
Telemetry
System Architecture
Personalization Systems
Content Understanding
Predictive Analytics