Machine Learning Engineer (Fintech)

TL;DR

Machine Learning Engineer (Fintech): Building and operating production systems for fraud detection products with an accent on reliability, scalability, and model deployment. Focus on developing ML pipelines, productionalizing data science models, and ensuring system performance under scale.

Location: Hybrid (Cary, North Carolina). Must be authorized to work in the U.S. without sponsorship

Company

Leading provider of digital banking and lending solutions for banks, credit unions, and fintechs in the U.S. and internationally.

What you will do

• Build and maintain systems and pipelines supporting training, evaluation, and inference for ML models.
• Deploy machine learning models into production environments and ensure reliability at scale.
• Write clean, maintainable, and well-tested code following production engineering best practices.
• Monitor and troubleshoot production ML systems, including data pipelines and model performance.
• Collaborate with data scientists and engineers to integrate models into scalable applications.
• Improve tooling and infrastructure supporting the ML development lifecycle.

Requirements

• Bachelor's degree in a relevant field and minimum of 2+ years of related experience.
• Proficiency in Python.
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Experience using version control (Git) and writing maintainable code.
• Fluent written and oral communication in English.
• Must be authorized to work for any employer in the U.S. (no sponsorship available).

Nice to have

• Experience building end-to-end ML systems including deployment and monitoring.
• Experience building APIs, backend services, or working with distributed systems.
• Familiarity with cloud platforms (AWS, GCP, or Azure).
• Exposure to MLOps concepts such as CI/CD.
• Experience with Typescript or other programming languages.

Culture & Benefits

• Hybrid work opportunities.
• Flexible time off.
• Competitive health insurance and generous paid parental leave.
• Career development and mentoring programs.
• Community volunteering and company philanthropy programs.