Manager, Machine Learning Engineering (Fraud)

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, and as a manager, you will lead a team of ML engineers to develop and improve models that detect and prevent fraud.

Responsibilities:

• 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

Requirements:

• 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 environments, including experimentation, evaluation, and model iteration in production
• Strong engineering fundamentals and experience working with scalable systems and data pipelines
• Track record of effective cross-functional collaboration with product, analytics, and engineering partners
• Ability to operate in ambiguous, fast-evolving environments and drive clarity, prioritization, and execution
• This position requires either equivalent practical experience or a Bachelor's degree in a related field