Machine Learning Engineer

As passionate about our people as we are about our mission.

Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our mission is simple: build strong and diverse communities through innovative financial technology—and we do that by empowering our people to help create success for our customers.

Being as passionate about our people as we are about our mission. We celebrate our employees in many ways, including our “Circle of Awesomeness” award ceremony and day of employee celebration among others! We invest in the growth and development of our team members through ongoing learning opportunities, mentorship programs, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun.

We hold an annual Dodgeball for Charity event at our Q2 Stadium in Austin, inviting other local companies to play, and community organizations we support to raise money and awareness together.

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.

• 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.

• Enjoy autonomy in your work and feel a sense of ownership in the team’s goals. You work quickly but with the big picture in mind.
• Have empathy for the end user and a desire to measure your work by both the customer value and technical quality.
• Have enthusiasm for the field and professional development.

• 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, Tensor Flow, scikit‑learn).

• 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).

• Hybrid Work…