Machine Learning Engineer

Join a high-growth financial technology organization focused on building modern digital banking, payments, lending, and risk solutions for financial institutions and fintech partners. This team is investing in machine learning and analytics capabilities to help improve fraud detection, predictive insights, and operational decision-making across customer-facing products.

This is an opportunity to work on applied machine learning systems that directly support real‑world fraud and risk workflows. The team owns solutions end-to-end and is focused on building scalable, production-ready ML applications that deliver measurable customer impact.

We are seeking a Machine Learning Engineer to help design, deploy, and support production machine learning systems within a collaborative engineering organization. This individual will work closely with software engineers, data scientists, and product teams to operationalize machine learning models, improve ML infrastructure, and support scalable analytics workflows.

This is a hands‑on engineering role focused on production systems, model deployment, APIs, pipelines, and ML operations rather than purely research-oriented machine learning work.

• Build and maintain systems and pipelines supporting machine learning training, evaluation, inference, and monitoring
• Deploy and support machine learning models in production environments
• Write clean, scalable, maintainable, and well-tested Python code
• Support monitoring, troubleshooting, and optimization of production ML systems and data pipelines
• Collaborate cross-functionally with engineering, data science, and product teams to operationalize ML solutions
• Improve the reliability, scalability, and performance of ML infrastructure and services
• Contribute to tooling and processes that support the machine learning development lifecycle
• Participate in code reviews, technical discussions, and collaborative problem solving

• 2+ years of experience in machine learning engineering, software engineering, or related technical experience
• Strong Python development experience
• Experience working with machine learning frameworks such as PyTorch, Tensor Flow, or scikit-learn
• Experience deploying or supporting machine learning models in production environments
• Experience writing clean, maintainable code and using version control tools such as Git
• Exposure to cloud platforms such as AWS, GCP, or Azure
• Understanding of taking machine learning models from research/development into production systems

• Hybrid work environment based in Cary, NC
• Applicants must be authorized to work in the U.S. without sponsorship
• Competitive compensation, benefits, flexible time off, and career development opportunities