Machine Learning Engineer, Fraud

Whatnot · Consumer · San Francisco, CA · Engineering

Machine Learning Engineer focused on designing, training, and deploying ML models and systems for fraud detection and prevention in a high-volume marketplace. This includes building user graphs, real-time inference systems, and monitoring, with a strong emphasis on end-to-end ownership and integrating ML into fraud orchestration.

What you'd actually do

  1. Design, train, and deploy both traditional ML and LLM-powered models to detect fraudulent behaviors across users, payments, and marketplace interactions.
  2. Lead the end-to-end architecture of fraud detection, prevention, and intervention systems — balancing platform security with a seamless user experience.
  3. Build intelligent user graphs to model behavioral patterns, collusion networks, and account connectivity.
  4. Develop scalable data pipelines and real-time inference systems supporting high-volume, low-latency ML workloads.
  5. Conduct deep behavioral and adversarial data analysis to uncover fraud trends and continuously improve detection accuracy.

Skills

Required

  • Python
  • scikit-learn
  • PyTorch
  • LightGBM
  • backend development
  • deploying ML models to production
  • SQL
  • Spark
  • DBT
  • fraud detection techniques
  • anomaly detection
  • graph-based modeling
  • data orchestration frameworks
  • feature store design

Nice to have

  • LLM-powered models

What the JD emphasized

  • end-to-end architecture
  • real-time inference systems
  • model monitoring
  • fraud detection
  • fraud trends

Other signals

  • design, train, and deploy models
  • end-to-end architecture of fraud detection, prevention, and intervention systems
  • build intelligent user graphs
  • develop scalable data pipelines and real-time inference systems
  • implement model monitoring and drift detection systems
  • fraud risk orchestration