Staff Machine Learning Engineer

Payabli is a next-generation Payments Infrastructure and Monetization Platform purpose-built for vertical software companies. Through a single, developer-friendly API with low-code embedded payment components, Payabli enables platforms to seamlessly embed, monetize, and operationalize payments—making payments a core part of their platform and business model.   By unifying payment acceptance, payment issuance, and advanced payment operations tooling, Payabli empowers software companies to manage and move money through a single infrastructure stack that delivers total control over the payments experience. Built to scale with PCI DSS 4.0 and SOC 2-compliant security, Payabli’s infrastructure delivers enterprise-grade reliability and trust while leveraging AI-driven intelligence to enhance visibility, streamline operations, and drive revenue growth.   Backed by leading fintech investors including QED Investors, Fika Ventures, TTV Capital, and Bling Capital, Payabli is setting the standard for embedded payments infrastructure powering the next generation of vertical SaaS.

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