Machine Learning Engineer

Q2 • Cary, NC

22h • Hybrid

About The Position

The Risk & Fraud team at Q2 helps our 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 our customers. Our solutions allow financial institutions to focus more of their time and energy on their mission: serving their customers and communities. As a Machine Learning Engineer, you will help build and operate production systems that power our fraud 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, and ongoing monitoring and improvements. This is an applied role – the software you build will be solving real problems for real customers, and will therefore need to be testable, reliable, and production-ready.

Requirements

• Typically requires a Bachelor’s degree in a relevant field and a minimum of 2+ years of related experience; or an advanced degree; or equivalent related work experience.
• Proficiency in Python.
• Experience writing clean, maintainable code and using version control (e.g., Git).
• Experience with machine learning and common frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Fluent written and oral communication in English.
• Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Nice To Haves

• 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 (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 other programming languages (e.g. Typescript).

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.

Benefits

• Hybrid Work Opportunities
• Flexible Time Off
• Career Development & Mentoring Programs
• Health & Wellness Benefits, including competitive health insurance offerings and generous paid parental leave for eligible new parents
• Community Volunteering & Company Philanthropy Programs
• Employee Peer Recognition Programs – “You Earned it”

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level