Senior Machine Learning Engineer (Fraud)

• 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
• 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

Benefits

• Vibrant offices in SF, NYC, and Raleigh-Durham—with catered meals, happy hours, and clubs to keep you connected
• Competitive pay, comprehensive health benefits, and support for fertility, mental health, and parental leave
• Lifestyle perks including home office stipends, daycare support, and commuting benefits like CitiBike and Lyft- 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 constructing training datasets and addressing label quality, data leakage, class imbalance, and generalization across time periods or populations
• 7+ years of professional experience in machine learning, applied science, or software engineering for ML, including hands-on model development and deployment
• Experience independently leading ML projects from an open-ended problem through deployment, coordinating requirements and model releases with Data Science, Product, and Engineering
• Strong ML and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing why a model underperforms
• Hands-on experience designing, training, tuning, and deploying models, and measuring improvements in production performance or business metrics
• Strong understanding of the strengths, limitations, and applications for both traditional and modern ML methods, including gradient-boosted trees and neural networks
• We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn’t fully match the job description
• 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