Job description

About Coinflow Coinflow is the next-generation payment service provider revolutionizing global financial infrastructure with stablecoins, AI-driven fraud prevention, and instant settlement. Coinflow enables businesses to grow faster with instant settlement, fraud & chargeback indemnity, global pay-ins, multi-currency FX, and unified payouts. Founded in 2023, the company serves marketplaces, fintechs, remittance providers, gaming platforms, and ecommerce merchants worldwide. Since our seed round in 2024, we’ve achieved 23x revenue growth and scaled to multi-billion-dollar annual transaction volume. In response to this growth, Coinflow announced a $25M Series A coinflow.cash in October 2025—led by Pantera Capital, CMT Digital, Coinbase Ventures, Jump Crypto, and Reciprocal Ventures—accelerating our mission to power the world’s fastest-moving businesses with innovative, reliable global payments. Coinflow is proudly headquartered in Chicago, IL. Learn more at coinflow.cash coinflow.cash. Role Overview: Coinflow is seeking a Machine Learning Lead to own the fraud and risk intelligence layer at the core of our platform. 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. KEY RESPONSIBILITIES - 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 REQUIRED QUALIFICATIONS - 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 What We Offer: - Competitive compensation including base salary, performance bonus, and meaningful ownership - Opportunity to build the fraud and risk intelligence layer of a rapidly scaling fintech company - Collaborative and innovative work environment with world-class investors - Direct impact on core risk infrastructure and company trajectory during a hyper growth phase The base salary range for this role is $225,000 - $275,000 USD. The actual base salary offered depends on a variety of factors, including but not limited to experience, education, skills, qualifications and business needs. In addition, the employee who fills this role will be eligible for an equity grant, allowing you to share in the long-term success of the company. You will also have access to a wide array of benefits, including health and wellness benefits, 401(k) savings plan, and flexible time off. Join the team rewriting how money moves worldwide—and become a driving force in the $194 trillion cross-border payments market.

Requirements

Must-haves

Coinflow is seeking a Machine Learning Lead to own the fraud and risk intelligence layer at the core of our platform.
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
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

Nice-to-haves

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

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aimachine learningml

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Benefits

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401(K)

Pto

Wellness