> Markdown version of [/jobs/ext/3272727-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/3272727-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer

- **Company:** Plaid's Network
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Information Leak Prevention, Database Queries, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Feature Engineering, Pytorch, Large Language Models, Model Validation, Generative AI, Scikit Learn, Low Latency, Xgboost, Machine Learning Operations
- **Published:** September 11, 2026
- **Apply:** https://startup.jobs/senior-machine-learning-engineer-fraud-plaid-com-10041397

## About the Role

* 7+ years of professional experience in machine learning, applied science, or software engineering for ML, including hands-on model development and deployment.
 * Hands-on experience designing, training, tuning, and deploying models, and measuring improvements in production performance or business metrics.
 * Strong ML and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing why a model underperforms.
 * Strong understanding of the strengths, limitations, and applications for both 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, SQL proficiency for working with training and evaluation data, and hands-on experience with ML frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents.
 * Experience independently leading ML projects from an open-ended problem through deployment, coordinating requirements and model releases with Data Science, Product, and Engineering.

Nice-to-Have:

 * Strongly preferred: 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, uncover fraud patterns, and improve fraud model performance.
 * Experience applying newer modeling approaches, such as learned representations, transformers, or foundation models, to improve a production ML use case.

## Description

As a Senior Machine Learning Engineer on Plaid's Fraud Data team, you will develop models that improve fraud detection for our customers. You will identify predictive patterns in Plaid's network data and lead projects from initial experiments through model deployment and ongoing improvement.

 * Investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage across customers and use cases.
 * Develop training datasets and predictive features, addressing challenges such as incomplete labels, class imbalance, data leakage, and changing fraud behavior.
 * Design, train, and tune models using traditional and modern ML methods, including gradient-boosted trees and neural networks, and evaluate newer architectures against existing approaches.
 * Design experiments to test features and models, comparing performance across time periods and customer segments using agreed detection and false-positive metrics.
 * Build data and training pipelines that support reproducible experiments and efficient iteration on features and models.
 * Deploy models with Engineering and ML Infrastructure partners, balancing detection quality, latency, cost, and reliability.
 * Independently lead ML projects, agreeing on priorities and evaluation metrics with Data Science and Product and coordinating work through model release.

Responsibilities:

 * Build hands-on machine learning expertise across the full ML lifecycle, from feature engineering and experimentation to model deployment.
 * Take models from initial experimentation through production and evaluate their impact using real-world customer outcomes.
 * Develop experience building and scaling reliable ML systems in production.
 * Explore how LLMs and Generative AI can improve fraud detection, prevention, and investigation.
 * Accelerate your career in a fast-paced environment with opportunities to take ownership, solve complex problems, and make a meaningful impact.