Founding ML Engineer — Real-Time Fraud & Risk at Scale

Coinflow; Chicago (IL); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Founder's equity

Job summary

Coinflow is building the first dedicated machine learning team to govern real-time risk decisions for payments. You will own the end-to-end lifecycle of real-time fraud detection, merchant underwriting, and fraud-loss prevention, leveraging proprietary data and external signals to maximize approval rates and minimize losses.

This founding role offers significant equity and a direct line from models to margins.

Qualifications

• 5+ years in machine learning, applied data science, or production ML roles.
• Direct experience building fraud models in payments.
• Experience as an early ML hire who built the function from scratch.
• Deep understanding of authorization fraud, card‑not‑present fraud, friendly fraud, and chargeback dynamics.

Responsibilities

• Design, build, and deploy real-time fraud detection and risk decisioning models across all payment methods.
• Own the complete model lifecycle — experimentation, feature engineering, deployment, monitoring, and retraining.
• Define and drive the metrics that matter: approval rate, detection rate, false positive rate, and chargeback rate.
• Hunt emerging fraud patterns and attack vectors in high-volume transaction data before they become losses.
• Build the feature store, data pipelines, and MLOps foundations the company will run on for years.
• Integrate and benchmark external fraud and risk data sources against our internal signals.
• Work directly with Engineering, Product, and Operations — and with the founders — to set long-term risk and ML strategy.
• Hire and grow the ML team as the function scales.

Skills

Fraud modeling

Payments domain

High-volume data analysis

Job description

Coinflow is building the first dedicated machine learning team to govern real-time risk decisions for payments. You will own the end-to-end lifecycle of real-time fraud detection, merchant underwriting, and fraud-loss prevention, leveraging proprietary data and external signals to maximize approval rates and minimize losses.

This founding role offers significant equity and a direct line from models to margins.