Staff Machine Learning Engineer

Join Payabli as a Staff Machine Learning Engineer, where you will set the technical direction for ML and build a broad portfolio of models to make payments smarter and easier. You will establish the ML foundations, translate ambiguous payments problems into modeling opportunities, and partner with product, engineering, and risk operations to own and prioritize the ML roadmap. Enjoy flexible PTO, stock options, and a monthly education fund.

Establecer la dirección técnica para el portafolio de modelos de aprendizaje automático de Payabli, tanto para mejorar los modelos existentes como para desarrollar nuevos modelos a lo largo del ciclo de vida de los pagos.

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

About Payabli

An interest in growing into people leadership as the function scales. We expect technical leadership from this role from the start; whether you want to manage a team down the road is genuinely up to you, and there's a clear path if you do

Responsibilities

• Establecer las bases de aprendizaje automático sobre las que el equipo construirá, incluyendo flujos de trabajo de experimentación, monitoreo de modelos, detección de desviaciones, evaluación del rendimiento y respuesta a incidentes.
• Traducir problemas ambiguos de pagos en oportunidades de modelado bien definidas y traducir el rendimiento del modelo en términos comerciales.
• 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
• Proven experience taking models from prototype to production and owning them post-launch (monitoring, retraining, incident response)
• 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
• Ability to communicate model behavior and business impact to non-ML stakeholders

Requirements

• Comfortable in a fast-moving startup environment; bias toward shipping
• 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
• Familiarity with AWS ML tools (e.g. SageMaker), and experience with feature stores, training/inference pipelines, and MLOps tooling

Preferred

• 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