Role Overview
Coinflow is seeking a full-time Machine Learning Lead to own the fraud and risk intelligence layer in Chicago. This role is ideal for an experienced machine learning professional looking to build and lead a dedicated team while shaping core payment security infrastructure.
Key Responsibilities
Build, deploy, and iterate on production fraud detection models and risk decisioning capabilities using advanced feature engineering.
Manage the complete lifecycle of models from initial experimentation and evaluation to continuous production monitoring.
Track and optimize key risk metrics, including detection rates, false positive rates, chargeback rates, and dispute win rates.
Analyze large volumes of transaction and behavioral data to uncover emerging fraud patterns and new detection signals.
Collaborate closely with Engineering, Product, and Operations to integrate smart fraud intelligence directly into payment workflows.
Integrate third-party risk vendors and orchestrate external tooling for optimal performance.
Establish fundamental data and machine learning practices to shape the long-term technological roadmap.
Requirements
Minimum of 5 years of professional experience working in applied data science, production machine learning, or related roles.
Practical background in constructing fraud models within the payments industry, specifically on the acquiring side such as an acquirer, payment facilitator, or PSP.
Demonstrated history of successfully transitioning machine learning projects from proof-of-concept stages to full production deployments.
Comprehensive understanding of acquiring-side fraud concepts like authorization fraud, chargeback patterns, card-not-present fraud, and merchant risk.
Solid grounding in statistics, machine learning principles, and feature engineering applied to high-volume financial data.
Ability to independently navigate ambiguous problems and establish organizational structure from scratch.
Nice to Have
Professional background working within an ISO, acquirer, PayFac, or broader payments infrastructure organization.
Direct experience designing, scaling, and managing MLOps pipelines alongside real-time monitoring and automated retraining schedules.
Experience estimating cloud computing resource needs for scalable machine learning workloads.
Working knowledge of card network regulations, fraud liability frameworks, and dispute workflows.
Previous experience operating as an early or sole machine learning engineer at an emerging startup.
Familiarity with near-real-time or real-time fraud scoring architectures alongside alternative payment or stablecoin rails.
Skills and Tools
Machine Learning
MLOps
Fraud Detection and Risk Modeling
Feature Engineering
High-Volume Financial Data Processing
Salary and Benefits
Base annual salary ranging from $225,000 to $275,000 USD, determined by qualifications, experience, and business needs.
Meaningful company ownership through equity grants.
Comprehensive health and wellness benefits.
Participation in a 401(k) savings plan.
Flexible time off policies.
About the Company
Coinflow operates as a next-generation payment service provider focused on revolutionizing global financial infrastructure through stablecoins, instant settlement, and AI-driven fraud prevention. Founded in 2023 and headquartered in Chicago, the firm serves marketplaces, ecommerce merchants, fintechs, and gaming platforms internationally. Backed by major investors following a 2024 seed round and an October 2025 Series A funding event, the organization scales rapidly to power high-volume global transactions.
How to Apply
Interested candidates should visit the official Coinflow careers page to submit an application for this position.
