Founding Machine Learning Engineer — Risk & Fraud

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 the payments infrastructure powering instant settlement for marketplaces, fintechs, remittance providers, gaming platforms, and ecommerce merchants worldwide. We combine stablecoin rails with AI-driven fraud prevention to move money faster than anyone else in the industry.

We've raised over $70M from investors including Pantera Capital, CMT Digital, Coinbase Ventures, Jump Crypto, and Reciprocal Ventures. We're profitable, we've grown revenue 23x since our seed round, and we process multi-billion-dollar annual transaction volume — making us one of the fastest-growing companies in payments.

The Role

About Coinflow

Coinflow is the payments infrastructure powering instant settlement for marketplaces, fintechs, remittance providers, gaming platforms, and ecommerce merchants worldwide. We combine stablecoin rails with AI-driven fraud prevention to move money faster than anyone else in the industry.

We've raised over $70M from investors including Pantera Capital, CMT Digital, Coinbase Ventures, Jump Crypto, and Reciprocal Ventures. We're profitable, we've grown revenue 23x since our seed round, and we process multi-billion-dollar annual transaction volume — making us one of the fastest-growing companies in payments.

The Role

This is a founding ML role. You'll lead our first dedicated machine learning team and own the systems that decide — in real time — which transactions we approve, which merchants we underwrite, and where fraud losses stop.

Today, risk decisions at our scale depend on a mix of internal signals and external partners. Your mandate is to build the in-house intelligence that makes those decisions better, faster, and entirely our own: models that combine our proprietary transaction data with external signals to maximize approval rates, sharpen merchant underwriting, and catch fraud before it costs us or our merchants a dollar.

You’ll ship models that score live payment volume from day one. The feedback loop is immediate and the impact is measured in basis points on billions of dollars.

What You’ll Do

• 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

What We’re Looking For

• 5+ years in machine learning, applied data science, or production ML roles
• Direct experience building fraud models in payments — acquiring‑side experience strongly preferred
• A track record of taking models from proof-of-concept to production systems scoring live traffic
• Deep understanding of authorization fraud, card‑not‑present fraud, friendly fraud, and chargeback dynamics
• Strong statistical and feature engineering fundamentals on high-volume financial data
• Comfort with ambiguity — you can scope and solve open‑ended problems without a playbook
• Strong cross‑functional communication; you can explain model tradeoffs to engineers, ops, and executives

Nice to Have

• Background at an acquirer, ISO, PayFac, payments infrastructure company, or fraud/risk vendor (e.g., decisioning, chargeback guarantee, or trust & safety platforms)
• Experience building real‑time (sub‑100ms) scoring systems
• MLOps pipeline development and model monitoring at scale
• Knowledge of card network rules and chargeback workflows
• Experience as an early ML hire who built the function from scratch
• Crypto or stablecoin familiarity
  

Why This Role Is Different

Most fraud ML jobs mean tuning someone else’s model inside a mature stack. Here you build the stack. Profitable company, founding‑team equity, billions in live volume to train on, and a direct line from your models to the company’s margins. Few ML roles anywhere offer this combination of greenfield ownership and immediate, measurable stakes.