# Senior Machine Learning Engineer - Fraud

**Company:** [Plaid](https://hotfix.jobs/companies/plaid)
**Location:** San Francisco, CA, Seattle, WA, New York, NY
**Role:** ML Engineering
**Salary:** $229k – $315k/yr
**Experience:** 7+ years
**Skills:** Python, SQL, PyTorch, scikit-learn, Xgboost, Feature Engineering, Neural Networks, Gradient-Boosted Trees, Experiment Design, Model Evaluation, Data Pipelines, Generative AI, Graph-Based Systems, Transformers, Fraud Modeling
**Posted:** 2026-09-12

> Develop and deploy machine learning models that detect fraud, from feature engineering and experimentation through production optimization. The role requires 7+ years of ML or software engineering experience, strong Python and SQL skills, and expertise in evaluating and scaling reliable models.

## Job Description

## Responsibilities
- Investigate fraud patterns and model errors to identify predictive signals, improve detection, and expand coverage across customers and use cases.
- Develop training datasets and predictive features while addressing incomplete labels, class imbalance, data leakage, and changing fraud behavior.
- Design, train, tune, and evaluate models using traditional and modern machine learning methods, including gradient-boosted trees and neural networks.
- Design experiments comparing features and models across time periods and customer segments using detection and false-positive metrics.
- Build data and training pipelines that support reproducible experiments and efficient iteration.
- Deploy models with Engineering and ML Infrastructure partners while balancing detection quality, latency, cost, and reliability.
- Lead machine learning projects from initial experimentation through model release and ongoing improvement.
- Evaluate model impact using real-world customer outcomes.
- Explore large language models and generative AI for fraud detection, prevention, and investigation.

## Requirements
- 7+ years of professional experience in machine learning, applied science, or software engineering for machine learning, including hands-on model development and deployment.
- Experience designing, training, tuning, and deploying models and measuring improvements in production performance or business metrics.
- Strong machine learning and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing model performance.
- Understanding of traditional and modern machine learning 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.
- Hands-on experience with machine learning frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents.
- Experience independently leading machine learning projects from open-ended problem definition through deployment and coordinating requirements and model releases with Data Science, Product, and Engineering.

## Nice-to-Haves
- Fraud or risk modeling experience, including familiarity with fraud patterns, delayed feedback, and the tradeoff between fraud detection and legitimate-user friction.
- Experience developing models that generalize across customers with different data and behavior patterns.
- Experience using graph-based systems to extract predictive signals and uncover fraud patterns.
- Experience applying learned representations, transformers, or foundation models to production machine learning use cases.

## Compensation and Benefits
- Annual salary range: $228,960–$315,360.
- Additional compensation may include equity and/or commission.
- Comprehensive benefits include medical, dental, vision, and 401(k).

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