Senior Applied Scientist | Microsoft Careers

Microsoft

Hybrid

Quick summary

Work type
Hybrid
Location
Salary
$119,800–$234,700 / yr
Posted
22 days ago
Closes
Nov 10, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $193k
This role $177k
$106k most similar roles pay here $248k

This role pays more than 62% of similar roles. Most pay $176,375–$208,800 — the shaded band above. At the midpoint, this role pays about $177k versus about $193k for comparable roles.

Based on 239 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 728 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 664 roles with salary data.

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At a glance

TL;DR · Senior Applied Scientist | Microsoft Careers

As a Senior Machine Learning Engineer in the Signals Modeling team, you will design and train transformer-based models with billions of parameters to predict user interactions with ads across large-scale consumer surfaces. Your daily tasks include driving modeling innovations for ad interaction outcome prediction under partial and noisy feedback, building estimated conversion models, and developing robust learning methods. You will work with Python, PyTorch or TensorFlow, and handle large datasets to create sophisticated training pipelines that transform weak signals into high-quality learning targets. The role involves deep expertise in deep learning, large-scale experimentation, and marketplace economics, requiring experience with noisy labels, conversion modeling, model calibration, and multi-task learning systems. You will contribute to one of the world’s largest ads ecosystems by shipping production-grade models and data pipelines that directly impact ad ranking, bidding, advertiser ROI, and user experience at web scale.

What you'll do

  • Drive innovations in modeling and data for predicting ad interaction outcomes with partial or noisy feedback.
  • Design and evaluate multi-task models using proxy signals and improve offline and online measurement frameworks.
  • Develop robust learning methods and calibration techniques for scenarios with sparse, delayed, or unobservable true user outcomes.
  • Build production-ready systems that translate modeling advances into direct impacts on ad ranking, bidding, and advertiser ROI.
  • Create data-driven attribution pipelines and generate weak labels from indirect user behavior to enhance model training signals.

What we're looking for

  • 4+ years industry experience building and shipping machine learning models in production.
  • Solid hands-on experience with modern ML models (e.g., deep learning) and feature engineering.
  • Experience working with noisy or weak labels and indirect user behavior data.
  • Familiarity with model calibration, reliability analysis, and uncertainty estimation.
  • Background in causal inference, attribution, and counterfactual evaluation.
  • Proven technical leadership in cross-team modeling efforts or platform-level ML systems.

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