ML Ops Engineer

About Coinflow Coinflow (fka Coinflow Labs) 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-in, multi-currency FX, and unified payouts—all in one intuitive platform. We serve marketplaces, fintechs, remittance providers, gaming platforms, and e-commerce merchants worldwide. Coinflow is proudly headquartered in Chicago, Illinois. Learn more @ coinflow.cash

Coinflow

The future of payments, delivered today.

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C

coinflow.cash

51-200 employees

Financial Services

Chicago, Illinois

Privately Held

15k LinkedIn followers

Raised $28.7M

• Real-Time Model Serving: Build and operate the inference layer that scores transactions in the authorization path. Hit tight latency budgets, design safe fallbacks when a model or feature is unavailable, and make sure a model outage never becomes a payments outage.
• ML Pipelines & Feature Store: Own training, validation, and deployment pipelines end to end. Build reproducible feature pipelines from our transaction, device, and chargeback data, with point-in-time correctness so offline training matches online scoring.
• Model Deployment & Rollout: Make shipping a model boring. Versioning, registries, shadow mode, champion/challenger, canary rollouts, and one-click rollback. Every change is gated by evals, not vibes.
• Monitoring & Drift Detection: Instrument models the way we instrument services. Track latency, score distributions, feature drift, approval rates, and fraud and chargeback outcomes, wired into our observability stack with alerts that fire on real problems.
• Third-Party Model Integration: Integrate and evaluate external fraud and risk vendors alongside our own models. Run parallel and shadow testing, compare performance on our data, and give the team a clear answer on what to trust.
• Merchant-Facing Risk Tooling: Power the data and scoring behind our merchant-facing risk products, so merchants can see risky customers and set their own blocking rules on top of our signals.

• 4+ years in MLOps, ML infrastructure, or ML platform engineering, ideally at a fintech, payments company, or other regulated environment
• You've run models in production with real latency and uptime requirements, not just trained them. Experience with real-time inference in a transaction or decisioning path is a big plus
• Strong Python and solid production engineering habits. Comfortable reading and writing TypeScript/Node, since that's where most of our services live
• Hands-on experience with ML pipeline and serving tooling (MLflow, Kubeflow, SageMaker, Vertex, BentoML, Feast, or similar) and the judgment to know when not to use them
• Solid infrastructure skills: containers, Kubernetes, CI/CD, and at least one major cloud provider
• Data engineering chops: you can build reliable feature pipelines over operational, warehouse, and streaming data, and you understand training/serving skew
• Experience monitoring models in production: drift, calibration, and tying model metrics back to business outcomes like fraud loss and approval rates
• Familiarity with fraud, risk, or credit modeling, and with explaining model behavior to non-ML stakeholders like banks and auditors
• Comfort working with AI-native tooling (Claude Code or similar) as a daily driver, or genuine excitement to start
• A bias toward shipping. We'd rather have a working v1 of a model pipeline today than a perfect platform next quarter
• The base salary range for this role is $120,000 - $180,000 USD + equity. The actual base salary offered depends on a variety of factors, including but not limited to experience, education, skills, qualifications, and business needs.
• Join the team rewriting how money moves worldwide—and become a driving force in the $194 trillion cross-border payments market.

• Health and wellness benefits
• 401(k) savings plan
• Flexible time off
• Equity grant