Staff Machine Learning Engineer

About the role

Job Description About Payabli 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 capabiliti…

Requirements

• 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
• 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

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