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 data science. You should have a proven track record of deploying production-grade models, specifically within risk, fraud, or trust and safety domains. The team is looking for someone who can transition comfortably between building scalable data pipelines and conducting deep adversarial analysis to catch bad actors in real-time. The compensation is highly competitive, starting at $245,000 plus equity and a 4% 401k match. While the role is "on-site," Whatnot operates as a remote-co-located team, offering a "best of both worlds" approach: hefty work-from-home stipends and flexibility, paired with local hubs in SF, NYC, LA, and Seattle for in-person collaboration. Unique perks include a monthly budget to "dogfood" the app by buying and selling collectibles. **You might be a good fit if you:** * Have experience building intelligent user graphs or anomaly detection systems to stop fraud. * Are proficient in Python and ML frameworks like PyTorch or LightGBM. * Can manage the full lifecycle of a model, from SQL/Spark ETLs to real-time inference and drift monitoring. * Want to work in a high-growth, fast-paced marketplace environment.