Manager, Machine Learning Engineering (Fraud)

Affirm is hiring a Manager of Machine Learning Engineering to lead the Fraud team in building and refining machine learning models that identify and prevent fraud during loan applications. This remote position is based in the US and oversees a team developing models across the full ML lifecycle—from feature engineering and experimentation through production deployment and ongoing monitoring—while balancing fraud prevention with customer approval rates and user experience.

Responsibilities

• Define technical and modeling strategy for fraud detection systems, aligning team deliverables with business objectives including fraud loss reduction, approval rates, and customer experience metrics
• Lead machine learning engineers to design, build, and iterate on fraud detection models across the complete ML lifecycle, from experimentation to production deployment
• Guide adoption and implementation of advanced modeling techniques, including representation learning, transformer-based methods, and deep learning approaches for behavioral pattern analysis
• Collaborate across Product, Fraud Analytics, Risk, and Platform Engineering teams to define solutions, evaluate technical and business trade-offs, and integrate models into decisioning systems
• Coach and develop team members through feedback and mentorship, fostering a culture focused on technical excellence and ownership

Requirements

• Bachelor's degree in a technical field
• 8+ years of industry experience in machine learning or related technical roles
• 3+ years managing engineers
• Hands-on experience with representation learning, deep learning, transformer-based models, and gradient-boosted tree methods
• Proven track record leading teams that delivered end-to-end ML solutions in production environments, including experimentation frameworks, model evaluation, and production iteration
• Experience building and working with scalable data systems and ML pipelines
• Experience in cross-functional collaboration with product, analytics, and engineering organizations