Machine Learning Engineer, Fraud at whatnot

This role is ideal for a mid-level Engineer (2–6 years of experience) who thrives at the intersection of backend engineering and machine learning. You should have a proven track record of deploying production-ready models, specifically within risk, fraud, or trust and safety domains. The team is looking for someone who can balance traditional ML (like LightGBM) with modern LLM-powered detection to secure a high-growth live marketplace. The compensation is highly competitive, ranging from **$245k to $345k**, which is top-of-market for mid-level talent. While the company operates with a "remote co-located" model, you must live within commuting distance of their hubs in **SF, NYC, LA, or Seattle**. Notable perks include a 4% 401k match, a dogfooding budget to shop on the platform, and comprehensive family-planning benefits. **You might be a good fit if you...** * Have experience building intelligent user graphs or anomaly detection systems to stop bad actors. * Are proficient in Python and comfortable handling data orchestrators like Dagster or Kubeflow. * Can manage the full lifecycle of a model, from SQL/Spark data pipelines to real-time inference. * Enjoy "adversarial" thinking—anticipating how fraudsters will pivot and building systems to stop them.