Software Engineer, Fraud at whatnot

This role is ideal for a mid-to-senior level engineer with at least 4 years of experience who thrives at the intersection of backend engineering and machine learning. You should have a solid foundation in Python and experience building high-traffic, reliable systems. The team is looking for someone with an "owner" mindset who can not only write production-grade code but also design the architecture for fraud detection systems and user behavioral modeling. The compensation is highly competitive ($170k - $230k) and comes with an extensive benefits package, including a 4% 401k match, 16 weeks of parental leave, and various monthly allowances for wellness, home office, and "dogfooding" the app. While the role is remote-friendly, it follows a "remote co-located" model, meaning you must live within commuting distance of their hubs in San Francisco, Los Angeles, Seattle, or New York for occasional in-person collaboration. **You might be a good fit if you...** * Have professional experience deploying ML models (PyTorch, LightGBM) to handle real-time, consumer-scale traffic. * Enjoy "adversarial" problem-solving—staying one step ahead of bad actors and botnets. * Are comfortable with data tooling like Snowflake, dbt, or Dagster to uncover behavioral patterns. * Prefer a high-growth, fast-paced environment where you can lead projects from architecture to deployment.