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Senior Machine Learning Engineer - Fraud

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Senior Machine Learning Engineer - Fraud in USA new

  • Seattle, WA

Responsibilities

  • Investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage.
  • Develop training datasets and predictive features while addressing incomplete labels, class imbalance, data leakage, and changing fraud behavior.
  • Design, train, tune, and evaluate models including gradient-boosted trees and neural networks.
  • Design experiments comparing model performance across time periods and customer segments using detection and false-positive metrics.
  • Build reproducible data and training pipelines for efficient experimentation and iteration.
  • Deploy models with Engineering and ML Infrastructure partners while balancing detection quality, latency, cost, and reliability.
  • Lead ML projects independently from initial experimentation through deployment, model release, and ongoing improvement.
  • Evaluate production model impact using real-world customer outcomes and explore LLMs and Generative AI for fraud detection, prevention, and investigation.

Requirements

  • 7+ years of professional experience in machine learning, applied science, or software engineering for ML.
  • Hands-on experience designing, training, tuning, deploying, and evaluating machine learning models in production.
  • Strong machine learning and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing underperforming models.
  • Understanding of traditional and modern ML methods, including gradient-boosted trees and neural networks.
  • Experience constructing training datasets and addressing label quality, data leakage, class imbalance, and generalization across time periods or populations.
  • Strong Python skills and SQL proficiency for training and evaluation data.
  • Hands-on experience with ML frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents.
  • Experience independently leading open-ended ML projects through deployment and coordinating requirements and releases with Data Science, Product, and Engineering.
  • Preferred experience in fraud or risk modeling, including delayed feedback and balancing fraud detection with legitimate-user friction.
  • Preferred experience developing models that generalize across customers and using graph-based systems to extract predictive signals.
  • Preferred experience applying learned representations, transformers, or foundation models to production ML use cases.

Benefits

  • Comprehensive medical, dental, vision, and 401(k) benefits.
  • Additional compensation may include equity and/or commission, depending on the position offered.
  • Pay and benefits are based on factors including scope, responsibilities, experience, skills, and location and may change under applicable plans.

Salary: $229k - $315k/yr

Plaid

Plaid builds APIs and SDKs that let developers and financial institutions connect consumer bank and investment accounts, verify ownership, retrieve transactions, assess income/liabilities, fight fraud, and move money (e.g., ACH/transfer). It sells usage-based access to products like Link, Auth, T...

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