Want to get hired at Microsoft?

Principal Machine Learning Engineer

Microsoft

Redmond, Washington, United StatesOn Site

Original Job Summary

About the Role

As a Principal Machine Learning Engineer at Microsoft, you will be part of the OneDrive & SharePoint (ODSP) Applied Science team. Your mission is to invent the AI‑native knowledge substrate for Microsoft 365. You will drive ideas from ideation to impactful results in a fast-paced environment.

Key Responsibilities

  • Design, build, and deploy large-scale machine learning and agentic systems.
  • Develop production-grade solutions including data pipelines, large-scale training, model serving, and performance optimization.
  • Collaborate with a team of passionate engineers and applied scientists.
  • Apply expertise in training or fine-tuning large language models, reinforcement learning, agentic AI architectures, and inference optimization.
  • Ensure compliance with Microsoft, customer, and government security screening requirements.

Security Requirements

This role requires passing the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Key skills/competency

  • Machine Learning
  • Large Language Models
  • Data Pipelines
  • Model Serving
  • Performance Optimization
  • Reinforcement Learning
  • Agentic AI
  • Applied Science
  • Security Compliance
  • Production Systems

How to Get Hired at Microsoft

🎯 Tips for Getting Hired

  • Customize your resume: Highlight ML systems and production experience.
  • Showcase projects: Emphasize large language model expertise.
  • Network strategically: Connect with Microsoft employees on LinkedIn.
  • Prepare for interviews: Practice technical questions and system design.

📝 Interview Preparation Advice

Technical Preparation

Review large language model training basics.
Practice optimizing model serving performance.
Study scalable data pipeline architectures.
Brush up on reinforcement learning techniques.

Behavioral Questions

Explain past collaboration in high-pressure environments.
Describe solving complex technical challenges.
Discuss balancing security and innovation.
Share experiences with continuous system improvement.