Senior Applied Scientist

Microsoft

Hybrid

Quick summary

Work type
Hybrid
Location
Salary
$119,800–$234,700 / yr
Posted
17 days ago
Closes
Dec 7, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $188k
This role $177k
$106k most similar roles pay here $248k

This role pays more than 51% of similar roles. Most pay $162,000–$213,931 — the shaded band above. At the midpoint, this role pays about $177k versus about $188k 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 · Senior Applied Scientist

The Core Search and AI team at Bing is seeking a senior machine learning engineer with expertise in building next-generation search technologies using advanced AI, particularly large language models. This role involves designing, training, and enhancing large-scale ML models for improving Bing's search relevance and ranking through transformer-based and LLM-powered methods. Day-to-day responsibilities include developing multi-stage ranking systems to ensure high-quality, low-latency results globally while leveraging LLMs for query understanding, document summarization, and representation learning. The ideal candidate will have a strong background in machine learning, NLP, and experience with large-scale system development for search or recommendation engines. They should be adept at addressing cold-start challenges and sparse data issues through innovative solutions, contributing to the team's mission of solving complex AI scalability problems on a massive scale.

What you'll do

  • Design and train large-scale machine learning models for Bing Search relevance and ranking.
  • Develop multi-stage ranking stacks to deliver high-quality, low-latency search results globally.
  • Optimize query and document understanding using LLMs to enhance ranking quality.
  • Address cold-start challenges through content understanding and pre-training techniques.
  • Conduct end-to-end model development from problem formulation to online A/B experimentation.

What we're looking for

  • Master's degree in a relevant field with 6+ years of experience or Ph.D. with 3+ years of experience.
  • Experience building and improving large-scale machine learning systems for search, ads, and recommendations.
  • Strong research background in machine learning, LLMs, and NLP.
  • Ability to design, train, and improve large-scale ML models for search relevance and ranking.
  • Expertise in developing multi-stage ranking stacks for high-quality, low-latency search results globally.
  • Experience presenting at industry conferences as an invited speaker.

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