Staff Machine Learning Engineer

Payabli is the Intelligent Fintech Operating System for software platforms. If you are a software company, you are a fintech company, and Payabli is your operating system. Built by payments industry and SaaS veterans and powered by its own models, Payabli gives platforms everything they need to embed, manage, and monetize financial services through a single developer-friendly API spanning Pay In, Pay Out, and Pay Ops. Intelligence is not a layer on top, it is the foundation, expressed through Amigo™, Payabli's native AI Agent Suite, and a growing set of capabilities that make payment operations smarter and more autonomous.

Payabli gives software companies PayFac capabilities without the heavy lift, administrative burden, or cost of becoming a payment facilitator themselves. With PayFac-as-a-Service, embedded payment acceptance and issuance, and intelligent payment operations, platforms turn payments into a meaningful revenue stream while staying focused on their core product.

Payabli is backed by world-class fintech investors including QED Investors, Fika Ventures, TTV Capital, and Bling Capital, and was recently named to the Forbes Fintech 50.

For more information, visit payabli.com http://payabli.com.

ABOUT THE ROLE

Payabli is looking for a Staff Machine Learning Engineer to set the technical direction for ML at Payabli. A few models are already live and driving real decisions: reducing time-to-clear for risk reviews and scoring transactions and merchants. But that's the starting line, not the destination. We want to build a broad portfolio of models that make payments smarter and easier: automatically choosing the best payment method, reducing disputes and chargebacks, improving authorization rates, forecasting payouts, and more. We need a technical anchor who can both raise the bar on what's live today and stand up many new models from scratch, owning evaluation, monitoring, feature development, retraining, and a clear line from model performance to business impact.

The decisions you make in your first quarter (how we build and ship models here, what "good" looks like for ML) will be foundational for years as the function scales. You'll partner closely with product, engineering, and risk operations to find where models create the most leverage across the payments lifecycle and define what "good" means for ML at Payabli.

WHAT YOU’LL DO

- Set the technical direction for Payabli's model portfolio - both maturing what's live (transaction risk, merchant risk) and building new models across the payments lifecycle (payment method optimization, dispute/chargeback reduction, authorization rate improvement, payout forecasting, and beyond)

- Establish the ML foundations the team will build on: experimentation workflows, model monitoring, drift detection, performance benchmarking, and incident response

- Translate ambiguous payments problems into well-scoped modeling opportunities, and model performance into business terms (loss rates, approval/auth rates, dispute rates, review efficiency)

- Raise the technical bar through influence and example: mentor ML engineers and set practices the future team inherits

- Partner with product, engineering, and risk operations to own and prioritize the ML roadmap

WHAT WE’RE LOOKING FOR

We're looking for someone who meets the minimum requirements below. If you meet them, we encourage you to apply. Your skills and trajectory matter more than checking every box.

- 8+ years of ML engineering experience, with 4+ years building and shipping production models that drive real business decisions

- A track record of owning modeling architecture and seeing big, hard-to-reverse decisions through to production

- Breadth across model types and problem framing. You can stand up a new model in an unfamiliar domain, not just optimize an existing one

- Proven experience taking models from prototype to production and owning them post-launch (monitoring, retraining, incident response)

- Deep grasp of modeling tradeoffs: precision/recall vs. operational cost, explainability, latency, and regulatory/compliance considerations

- Experience establishing ML processes and infrastructure that a growing team inherits

- A high technical bar set through influence and example. You make the work and the people around you better, and you're as comfortable in the codebase as in a design review

- Ability to communicate model behavior and business impact to non-ML stakeholders

- Comfortable in a fast-moving startup environment; bias toward shipping

NICE TO HAVES

- Payments, fintech, or lending experience: chargebacks, merchant risk, KYC/KYB, authorization/routing, or similar domains. Experience with risk, underwriting, fraud, or credit models specifically is a big plus

- Familiarity with AWS ML tools (e.g. SageMaker), and experience with feature stores, training/inference pipelines, and MLOps tooling

- An interest in growing into people leadership as t