Senior Machine Learning Engineer, Fraud

Jobtailor; California (MO); On-site; USD 120,000 - 170,000; Full time

Job summary

Jobtailor is seeking an experienced ML Engineer to advance fraud detection systems. You will design and deploy models, build robust training pipelines, and collaborate with Data Science, Product, and Engineering teams to scale solutions across customers.

Responsibilities include feature engineering, model evaluation, and exploring transformer-based approaches to improve production performance, with emphasis on reducing false positives and maintaining throughput.

Qualifications

• Strong ML fundamentals and feature engineering.
• Experience designing, training, tuning, deploying models.
• Experience with evaluating production performance.
• Hands-on Python programming.
• SQL proficiency for data handling.
• Experience with PyTorch / scikit-learn / XGBoost.
• Fraud or risk modeling experience preferred.

Responsibilities

• Investigate fraud patterns and model errors to identify signals and improve detection.
• Develop training datasets and predictive features addressing label quality and imbalance.
• Design, train, and tune models using gradient-boosted trees and neural networks.
• Evaluate newer model architectures against existing approaches.
• Lead ML projects from experiments through model release with teams.
• Deploy models with engineering and ML infrastructure partners.
• Explore LLMs and Generative AI for fraud detection and investigation.

Job description

• 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 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
• Evaluate newer model architectures against existing approaches
• Design experiments to compare feature and model performance across time periods and customer segments using detection and false-positive metrics
• Build data and training pipelines supporting reproducible experiments and efficient iteration
• Deploy models with Engineering and ML Infrastructure partners, balancing detection quality, latency, cost, and reliability
• Independently lead ML projects from initial experiments through model release, aligning priorities and evaluation metrics with Data Science and Product
• Take models through production and evaluate impact using real-world customer outcomes
• Develop and scale reliable ML systems in production
• 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, 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 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
• 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
• Fraud or risk modeling experience is strongly preferred
• 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 learned representations, transformers, or foundation models to improve a production ML use case

Core Competencies

Demonstrates extensive experience in machine learning model development, deployment, and evaluation, with a strong focus on fraud detection and prevention. Proficient in designing experiments, feature engineering, and utilizing modern ML methods to enhance model performance across diverse customer behaviors.

Highest-signal resume keywords

• Machine Learning Model Development
• Fraud Detection and Prevention
• Python Programming
• SQL Proficiency
• ML Frameworks (PyTorch, scikit-learn, XGBoost)

ATS Optimization Keywords

Hard Skills

• Model Training
• Model Tuning
• Feature Engineering
• Experiment Design
• Model Evaluation
• Data Leakage Management
• Class Imbalance Handling
• Graph-Based Systems
• Transformers
• Foundation Models

Soft Skills

• Project Leadership
• Collaboration
• Problem Solving

Industry Keywords

• Fraud Modeling
• Risk Modeling
• Predictive Features
• Customer Segmentation
• Generative AI

Tools & Technologies

• ML Infrastructure
• Data Pipelines
• Reproducible Experiments