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# Machine Learning Engineer - Fraud

- **Company:** COINFLOW LLC
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $225,000.0 - $275,000.0
- **Contract:** Permanent contract
- **Skills:** Cloud Computing, Fraud Prevention and Detection, Monitoring of Systems, Machine Learning, Software Deployment, Transaction Data, Feature Engineering, Machine Learning Operations
- **Published:** October 30, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-fraud-coinflow-8126538

## About the Role

* 5+ years in machine learning, applied data science, or production ML roles
 * Demonstrated experience building fraud models in payments, with direct exposure to the acquiring side - acquirer, PSP, or payment facilitator
 * Proven track record taking ML projects from proof-of-concept to fully deployed, productionized systems
 * Deep familiarity with acquiring-side fraud dynamics: authorization fraud, card-not-present fraud, friendly fraud, chargeback patterns, and merchant risk
 * Strong foundation in ML, statistics, and feature engineering on high-volume financial data
 * Comfortable owning ambiguous problems end-to-end and creating structure where none exists
 * Strong collaborator across Engineering, Product, and Ops

Preferred Qualifications

 * Experience at an acquirer, ISO, PayFac, or payments infrastructure company
 * Experience developing, managing, and scaling MLOps pipelines and monitoring systems (retraining schedules, real-time performance metrics)
 * Experience scoping cloud compute requirements for scalable ML workloads
 * Familiarity with card network rules, dispute/chargeback workflows, and fraud liability frameworks
 * Experience as an early or sole ML hire at a startup
 * Exposure to real-time or near-real-time fraud scoring systems
 * Experience with stablecoin, crypto, or alternative payment rails

## Description

This is a founding ML role. You'll lead our first dedicated ML team, building capabilities that combine our first-party transaction data with partner signals to optimize approval rates across payment methods and geographies, sharpen risk decisioning during merchant underwriting, and improve fraud detection across global payment methods. That means digging into large-scale transaction and behavioral data, shipping production fraud models, defining what good looks like, and continuously raising the bar on detection and precision as our volume and merchant base scale.

The ideal candidate has hands-on experience building fraud models on the acquiring side of payments and working alongside external fraud vendors to tackle card-present or card-not-present fraud, authorization decisioning, chargeback reduction, and related risk systems., * Strengthen Coinflow's fraud detection and risk decisioning capabilities - feature engineering, model development, and production deployment

 * Own the full model lifecycle: experimentation, evaluation, monitoring, and iteration
 * Define and track core fraud and risk metrics - detection rate, false positive rate, chargeback rate, dispute win rate - and continuously improve them
 * Explore transaction and behavioral data to surface new fraud signals and emerging attack patterns
 * Partner with Engineering, Product, and Operations to embed fraud intelligence directly into payment flows and internal tooling
 * Integrate and orchestrate external fraud/risk partners, getting maximum value from their tooling
 * Establish the foundation for ML and data practices across the company
 * Help shape Coinflow's long-term fraud, risk, and ML roadmap