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Machine Learning Engineer

William Bergmann

Full-time · Job · Chicago

About the role

Our fraud detection models block $2M in fraudulent transactions every week for mid-size e-commerce companies. That number should be higher. We know there are patterns our current models miss, especially in account takeover attacks and synthetic identity fraud, and we need an ML engineer who can find them. You'll work on the core detection pipeline: feature engineering from raw transaction streams, training and evaluating ensemble models, and deploying updates to our real-time scoring API. The feedback loop is tight — you'll see your model changes reflected in blocked fraud within days. The team is 4 ML engineers and 2 data engineers. We use PyTorch for deep models, scikit-learn for gradient boosting ensembles, and AWS for everything. You'll own features end-to-end, from hypothesis to production.

Responsibilities

  • Build and iterate on fraud detection models using ensemble and deep learning approaches
  • Engineer features from high-volume transaction streams
  • Deploy model updates to real-time scoring APIs on AWS
  • Analyze false positive and false negative patterns to improve detection
  • Own the model evaluation framework and A/B testing pipeline

Requirements

  • 3+ years building ML models for production systems
  • Experience with both scikit-learn and PyTorch
  • Strong SQL skills for feature engineering on large datasets
  • Familiarity with Docker and AWS deployment
  • Bonus: experience in fraud detection or financial services

Benefits

  • Fully remote within US time zones
  • Health and dental insurance
  • Annual bonus based on fraud prevention impact
  • $2,500 professional development budget

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Categories

AI & Machine Learning Engineers

Skills

python pytorch scikit-learn docker aws

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

AI & Machine Learning Engineers

Skills

python pytorch scikit-learn docker aws sql

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