Software Engineer, Fraud

Whatnot · Consumer · San Francisco, CA · Engineering

Software Engineer role focused on building and deploying ML-driven systems for fraud detection, prevention, and intervention in a real-time, high-volume marketplace. Responsibilities include developing intelligent user graphs, training and deploying traditional ML and LLM models, creating scalable data pipelines, and implementing human-in-the-loop systems to adapt to evolving fraud tactics.

What you'd actually do

  1. Lead the full architecture of fraud detection, prevention, and intervention systems — spanning machine learning, backend, and client-side components.
  2. Build intelligent user graphs to model behavioral patterns, detect collusion networks, and uncover account connectivity at scale.
  3. Design, train, and deploy both traditional ML and LLM-powered models to detect fraudulent activity across users, payments, and marketplace interactions.
  4. Develop scalable data pipelines and real-time inference systems capable of supporting high-volume, low-latency ML workloads.
  5. Create human-in-the-loop systems that continuously refine detection accuracy and adapt to evolving adversarial tactics.

Skills

Required

  • 4+ years of software engineering experience building systems for consumer-scale traffic and reliability
  • 1+ years writing production-grade Python code and working with ML libraries (e.g. PyTorch, LightGBM)
  • 1+ years of experience in machine learning or fraud prevention domains
  • Fluency with data tooling, including data warehouses (e.g. Snowflake) and transformation frameworks (e.g. dbt, Dagster)
  • Strong communication skills and the ability to lead initiatives across product areas, collaborating closely with leadership, data science, and product teams.

Nice to have

  • Hands-on machine learning or data science experience in production environments.

What the JD emphasized

  • ML-driven systems
  • real-time detection
  • LLM-powered models
  • high-volume, low-latency ML workloads
  • human-in-the-loop systems
  • adaptive, production-ready systems

Other signals

  • ML-driven systems
  • real-time detection
  • user-centered enforcement
  • behavioral patterns
  • collusion networks
  • account connectivity
  • LLM-powered models
  • fraudulent activity
  • high-volume, low-latency ML workloads
  • human-in-the-loop systems
  • emerging fraud trends
  • adaptive, production-ready systems