Principal Applied Scientist

Microsoft

Quick summary

Work type
On-site
Location
Redmond, WA
Salary
$142,800–$274,800 / yr
Posted
59 days ago
Closes
Oct 26, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $198k
This role $209k
$127k most similar roles pay here $291k

This role pays more than 59% of similar roles. Most pay $162,000–$234,150 — the shaded band above. At the midpoint, this role pays about $209k versus about $198k 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 622 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 571 roles with salary data.

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At a glance

TL;DR · Principal Applied Scientist

As a Principal Applied Scientist on the Microsoft Copilot team, you will lead the development of machine learning and generative AI systems that power product discovery, ranking, recommendation, and personalization across conversational shopping experiences. Your day-to-day responsibilities include designing and building large-scale models using commerce and behavioral data, developing LLM-based systems for conversational interactions, addressing quality and trust challenges, defining evaluation frameworks, and partnering with cross-functional teams to ensure low-latency user experiences. You will also provide technical leadership through design reviews and mentoring while contributing to model governance and Responsible AI practices. The role requires expertise in Python or R for data analysis, proficiency with Apache Spark or Azure Databricks, and experience with LLMs and domain adaptation techniques, particularly in handling privacy and compliance issues within the context of large-scale commerce systems.

What you'll do

  • Design and build machine learning models for product discovery, ranking, recommendation, and personalization.
  • Develop conversational shopping systems using large language models and retrieval-augmented generation techniques.
  • Define evaluation frameworks to measure the effectiveness of conversational and proactive shopping scenarios.
  • Address quality and trust issues in AI-driven commerce solutions to mitigate risks like hallucination and stale data.
  • Provide technical leadership by mentoring, conducting design reviews, and setting quality standards for applied science projects.

What we're looking for

  • Extensive hands-on experience developing machine learning models for real-world applications.
  • Proficiency in Python or R for data analysis and modeling, with SQL for data querying.
  • Experience with large-scale data processing tools like Apache Spark or Azure Databricks.
  • Expertise in time-series analysis and anomaly detection techniques for business insights.
  • Practical knowledge of prompt engineering and fine-tuning GPT-like models for domain-specific applications.

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