Af
Posted:
June 04, 2026
Location:
toronto, on, Canada
Job Description
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.
The Fraud Machine Learning team builds models that power critical decisions during the loan application process, protecting the company 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 decision‑making systems. You will also help drive th...
The Fraud Machine Learning team builds models that power critical decisions during the loan application process, protecting the company 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 decision‑making systems. You will also help drive th...
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Submit your application for the Manager, Machine Learning Engineering (Fraud) position at Affirm.
Apply Now Save for LaterJob Overview
Job Type:
Full-time
Location:
toronto, Canada
Posted:
June 04, 2026
Deadline:
July 14, 2026