The Risk & Fraud team helps customers take a proactive stance against fraud while managing the risks inherent to their business. We build and enhance products that evolve with the ever-changing fraud landscape, delivering tangible value to customers. Our solutions allow financial institutions to focus more of their time and energy on serving their customers and communities.

As a Machine Learning Engineer, you will help build and operate production systems that power fraud detection and risk-related products. You’ll work closely with data scientists and engineers to bring models into production, ensuring they are reliable, scalable, and maintainable.

You’ll gain hands-on experience working across model development, evaluation, deployment, monitoring, and ongoing improvements. This is an applied engineering role — the software you build will solve real-world problems and must be production-ready, reliable, and testable.

A Typical Day

Your Key Responsibilities

• Build and maintain systems and pipelines that support training, evaluation, and inference for machine learning models

• Contribute to deploying machine learning models into production environments and ensuring they run reliably at scale

• Write clean, maintainable, and well-tested code following production engineering best practices

• Support monitoring and troubleshooting production ML systems, including data pipelines and model performance

• Collaborate with data scientists and engineers to productionalize models and integrate them into scalable applications

• Help improve the reliability, scalability, and performance of ML systems over time

• Contribute to improving tooling and infrastructure that supports the ML development lifecycle

You Are More Likely to Excel If You:

• Enjoy autonomy in your work and take ownership of team goals while balancing speed with long-term impact

• Have empathy for end users and measure success through both customer value and technical quality

• Are enthusiastic about machine learning, engineering excellence, and continuous professional development

Bring Your Passion, Do What You Love. Here’s What We’re Looking For

Must-Haves

• Bachelor’s degree in a relevant field with 2+ years of related experience, or equivalent practical experience

• Proficiency in Python

• Experience writing clean, maintainable code and using version control tools such as Git

• Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn

Nice to Have

• Experience building end-to-end ML systems, including data pipelines, model training, deployment, and monitoring

• Experience deploying or integrating machine learning models into applications

• Experience building APIs, backend services, or working with distributed systems

• Familiarity with cloud platforms such as AWS, GCP, or Azure

• Exposure to MLOps concepts such as CI/CD and model monitoring

• Experience working with large datasets or data processing frameworks

• Experience with additional programming languages such as TypeScript