Machine Learning Engineer

About the Role

As a Machine Learning Engineer on the Risk & Fraud team at Q2, you'll build and operate production systems behind fraud detection at scale, helping protect nearly two trillion dollars in transactions for millions of users each year. You'll work closely with data scientists and engineers to turn models into reliable, real-time systems and continuously improve how they perform in production. This is an applied role: the software you build will be solving real problems for real customers, and will therefore need to be tested rigorously.

Responsibilities

• Research emerging fraud and abuse patterns and translate that research into new detection approaches
• Help build next-generation ML products across identity, behavior, and transaction fraud, partnering directly with customers to understand their needs and shape product direction
• Build and optimize real-time, low-latency ML infrastructure, continually improving its reliability, scalability, and performance
• Build and maintain systems and pipelines that support training, evaluation, and inference for machine learning models, collaborating with data scientists to productionalize models into scalable applications
• Write clean, maintainable, and well-tested code, following production engineering best practices and leveraging the latest AI tooling
• Support monitoring and troubleshooting of production ML systems, including data pipelines and model performance

What We're Looking For

• Bachelor's degree in related field and 2+ years of relevant experience
• Proven experience in ML model development and deployment
• Strong knowledge of statistics, optimization, probability theory, and experimental methodologies
• Proficiency in programming languages such as Python, R, or Java
• Experience with ML frameworks/libraries (TensorFlow, PyTorch, scikit-learn)
• Familiarity with cloud platforms and scalable computing resources
• Strong analytical, problem-solving, and collaboration skills

Nice to Have

• Experience applying machine learning to fraud detection, risk modeling, or a closely related domain
• Experience building end-to-end ML systems, from data pipelines and model training through deployment and monitoring, including integrating models into applications at scale
• Experience building APIs, backend services, or working with distributed systems
• Experience working with large datasets or data processing frameworks
• Comfort using AI-assisted development tools (e.g., Claude Code, Copilot) to accelerate and improve engineering work

Benefits

• Hybrid work opportunities
• Flexible time off
• Career development and mentoring programs
• Health and wellness benefits, including competitive health insurance offerings and generous paid parental leave for eligible new parents
• Community volunteering and company philanthropy programs
• Employee peer recognition programs ("You Earned It")