Manager, Machine Learning Engineering (Fraud)

Affirm is seeking a Manager of Machine Learning Engineering to lead the Fraud detection team remotely from Canada. This role oversees ML engineers building and refining models that protect Affirm and its customers during loan origination while preserving a smooth application experience. You will own the technical strategy for fraud detection, shepherd models through the full ML lifecycle, and partner across Product, Analytics, Risk, and Platform teams to integrate solutions into live decisioning systems.

Responsibilities

• Define technical and modeling strategy for fraud detection systems, aligning team roadmap with business priorities including fraud loss reduction, approval rates, and customer experience metrics
• Lead machine learning engineers through design, development, and iteration of fraud detection models across experimentation, validation, and production deployment phases
• Guide adoption of advanced modeling techniques including representation learning, transformer-based methods, and deep learning to capture complex behavioral patterns
• Collaborate cross-functionally with Product, Fraud Analytics, Risk, and Engineering teams to scope requirements, evaluate trade-offs, and ensure effective model integration into decisioning infrastructure
• Develop team members through coaching, feedback, and cultivation of a high-performance culture centered on technical rigor and ownership

Requirements

• Bachelor's degree in a technical field
• 8+ years of industry experience in machine learning or related technical roles
• 3+ years of experience managing engineering teams
• Hands-on expertise with modern ML approaches: representation learning, deep learning, transformer-based architectures, and gradient-boosted tree methods
• Demonstrated experience delivering end-to-end ML solutions in production, including experimentation design, model evaluation, and production iteration
• Strong software engineering fundamentals and experience with scalable systems, data pipelines, and distributed computing
• Proven track record of cross-functional collaboration with product, analytics, and engineering stakeholders