Manager, Machine Learning Engineering (fraud)

Affirm Affirm · Fintech · United States · Remote · Checkout

Manager of Machine Learning Engineering focused on fraud detection in a fintech setting. The role involves leading a team to develop and deploy ML models for fraud prevention, covering the full ML lifecycle from feature development to production monitoring. Emphasis on advanced techniques like representation learning and transformers, and cross-functional collaboration.

What you'd actually do

  1. 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
  2. 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
  3. Drive the evolution of modeling approaches, including the adoption of representation learning, transformer-based methods, and other advanced techniques for modeling complex behavioral data
  4. 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
  5. Develop talent by coaching engineers, providing feedback, and fostering a high-performing team culture grounded in technical excellence and ownership

Skills

Required

  • Bachelor’s in a technical field
  • 8+ years of industry experience
  • 3+ years managing engineers
  • modern ML approaches
  • representation learning
  • deep learning
  • transformer-based models
  • gradient-boosted trees
  • leading teams delivering end-to-end ML solutions
  • experimentation
  • evaluation
  • model iteration in production
  • scalable systems
  • data pipelines
  • cross-functional collaboration
  • ambiguous, fast-evolving environments

What the JD emphasized

  • full ML lifecycle
  • production deployment
  • model iteration in production
  • representation learning
  • transformer-based models
  • advanced techniques

Other signals

  • fraud detection models
  • production deployment
  • monitoring
  • representation learning
  • transformer-based techniques