Manager, Machine Learning Engineering (Fraud)

The Fraud Machine Learning team builds models that power critical decisions during the loan application process, protecting Affirm and its customers from fraud while maintaining a seamless user experience. As a manager, you will lead a team of ML engineers developing and improving models that detect and prevent abuse in a rapidly evolving, adversarial environment.

In this role, you will define the technical and modeling strategy for fraud detection, guiding the team across the full machine learning lifecycle from feature development and experimentation to production deployment and monitoring. You will partner closely with Product, Fraud Analytics, Risk, and Platform teams to ensure high-quality models are effectively integrated into decisioning systems. You will also help drive the evolution of modeling approaches at Affirm, including the adoption of representation learning and transformer-based techniques to better capture complex behavioral patterns.

What you’ll do

• Set the technical and modeling strategy for fraud detection, aligning team efforts with key business outcomes such as fraud loss reduction, approval rates, and customer experience

• Lead a team of machine learning engineers to design, build, and iterate on high-impact fraud models across the full ML lifecycle, from experimentation to production

• Drive the evolution of modeling approaches, including the adoption of representation learning, transformer-based methods, and other advanced techniques for modeling complex behavioral data

• Partner cross-functionally with Product, Fraud Analytics, Risk, and Engineering to define solutions, evaluate trade-offs, and ensure models are effectively integrated into decisioning systems

• Develop talent by coaching engineers, providing feedback, and fostering a high-performing team culture grounded in technical excellence and ownership

What we look for

• Bachelor’s in a technical field with 8+ years of industry experience, including 3+ years managing engineers

• Experience with modern ML approaches, including representation learning, deep learning, or transformer-based models, as well as traditional methods such as gradient-boosted trees

• Proven ability to lead teams delivering end-to-end ML solutions in production environmen