About Split Pay

Split Pay is a consumer fintech platform that makes paying housing expenses simple, predictable, and secure. Customers can split their largest recurring bills into installments while building credit at no cost. Our platform is deeply data-driven and relies on machine learning to power underwriting, fraud detection, risk management, and personalized customer experiences.

The Role

We are seeking a Senior Machine Learning Engineer to design, build, and operate production ML systems that support critical product and risk decisions. You will work closely with engineering, data, product, and risk teams to develop models and infrastructure that directly impact approval rates, losses, and customer outcomes.

This is a hands-on role focused on applied machine learning, strong software engineering, and reliable ML operations in a regulated fintech environment.

Responsibilities

• Design and deploy machine learning models for underwriting, fraud detection, and risk assessment.

• Build and maintain training pipelines, feature engineering workflows, and model serving infrastructure.

• Integrate ML models into backend services and data pipelines with a focus on reliability and scalability.

• Monitor model performance, data quality, and drift in production; iterate based on results.

• Partner with product, engineering, and risk stakeholders to define success metrics and model requirements.

• Contribute to ML engineering best practices, code reviews, and technical standards.

Requirements

• 5+ years of experience in machine learning engineering or applied data science.

• Proven experience deploying and operating ML models in production environments.

• Strong Python skills and experience with common ML frameworks (scikit-learn, PyTorch, or TensorFlow).

• Solid software engineering fundamentals and experience building APIs and data pipelines.

• Familiarity with cloud infrastructure, containerization, and CI/CD workflows.

Bonus Qualifications

• Experience in fintech, credit, payments, or fraud modeling.

• Familiarity with MLOps tooling and model monitoring frameworks.

• Experience with large-scale structured data and experimentation.

What Sets Split Pay Apart

• Direct ownership over ML systems that drive core product and risk decisions.

• Close collaboration with product and engineering in a fast-moving environment.

• A culture focused on data, accountability, and measurable impact.