Principal Applied Scientist

Microsoft

Hybrid

Quick summary

Work type
Hybrid
Location
—
Salary
$139,900–$274,800 / yr
Posted
79 days ago

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $198k
This role $207k
$124k most similar roles pay here $291k

This role pays more than 54% of similar roles. Most pay $162,000–$234,150 — the shaded band above. At the midpoint, this role pays about $207k 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

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As a Principal Applied Scientist on the Signals Team at Microsoft Ads, you will design and implement large-scale machine learning models to predict user responses to advertisements, enhancing engagement and maximizing advertiser returns. Your day-to-day responsibilities include building and deploying machine learning models for text and numerical data, conducting A/B testing, analyzing model metrics, and establishing scalable pipelines for data processing and analytics. You will also mentor less experienced scientists on the team and stay updated with state-of-the-art approaches to improve the modeling stack. The role requires expertise in designing, training, and optimizing large-scale machine learning models, particularly transformer-based models and large language models (LLMs), along with experience in LLM inference pipelines and advanced fine-tuning techniques. Strong skills in model quality evaluation, including automated metrics and human evaluation, are essential for this complex, multi-layered environment.

Skills

Python TensorFlow PyTorch Kubernetes Docker AWS Azure CI/CD Prometheus Grafana PostgreSQL Redis Git Jupyter Scikit-learn Pandas NumPy Transformer Models Large Language Models

What you'll do

  • Design and implement large-scale machine learning models for text and numerical data.
  • Deploy machine learning models in production environments and monitor their performance.
  • Conduct A/B testing to analyze the impact of model metrics on business KPIs.
  • Develop scalable pipelines for data processing, analytics, training, and validation.
  • Track state-of-the-art approaches and implement them to improve existing modeling stack.
  • Mentor less experienced scientists on the team in machine learning practices.

What we're looking for

  • Deep experience designing, training, and optimizing large-scale machine learning models.
  • Proven ability to build high-performance LLM inference pipelines and apply advanced fine-tuning techniques.
  • Strong expertise in developing robust evaluation frameworks for ML and LLM systems.
  • Experience with transformer-based models and production deployment of machine learning models.
  • 5+ years of customer-facing, project-delivery experience in professional services or consulting.

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