Two founders from payment infrastructure. One shared experience watching fraud tools fail in production.
CEO & Co-Founder
Previously VP of Platform Engineering at PayEdge, where she led payment processing infrastructure handling $700M annually. Before that, led fraud systems at Marqeta for 4 years. Holds an MS in Computer Science from Carnegie Mellon. Deep expertise in payment authorization flows and card network protocols.
CTO & Co-Founder
Former Principal ML Engineer at Stripe, where he spent 5 years building Radar's ensemble fraud models. Published research on temporal graph networks for financial fraud detection at NeurIPS 2022. Architected InferX's real-time feature extraction pipeline and the adaptive model retraining system.
Everyone on the team has been the person getting paged at 2AM because a fraud spike hit and the detection system missed it. That experience shapes how we build.
We are 12 people. No sales team yet, no dedicated marketing. The people who talk to customers are the same people who read their error logs and improve the model on their data.
Headquarters in Palo Alto. About half the team works remotely across US time zones. Quarterly in-person engineering sprints. No mandatory office days.
We are growing. If you have a background in payment systems, ML for financial services, or distributed systems at scale — and you find fraud detection genuinely interesting — we want to talk.
Gradient boosting, feature engineering on financial time series, experience with XGBoost or LightGBM in production. Bonus: real-time model serving at sub-100ms.
Experience with payment APIs (Stripe, Adyen, or similar), high-throughput API design, Go or Rust preferred. Experience with Kafka or similar streaming infrastructure a strong plus.
Experience reviewing chargebacks and dispute data at a payment processor or acquiring bank. Understanding of reason codes, BIN intelligence, and card-present vs CNP fraud patterns.