About this role
Q2’s Risk & Fraud team is seeking a Machine Learning Engineer to build and operate production fraud-detection systems for financial institutions. The systems help protect nearly two trillion dollars in transactions for millions of users each year. This applied role focuses on turning models into reliable, real-time applications and improving their performance, latency, and reliability in production.
Responsibilities
- Research emerging fraud and abuse patterns and develop detection approaches for identity, behavior, and transaction fraud. Work with customers to understand their needs and help shape ML products.
- Build and optimize scalable, low-latency ML infrastructure and pipelines for model training, evaluation, and inference. Collaborate with data scientists and engineers to deploy models in production.
- Write maintainable, rigorously tested code; monitor and troubleshoot production ML systems, data pipelines, and model performance.
Required qualifications
- Bachelor’s degree in a related field and 2+ years of relevant experience, including demonstrated ML model development and deployment.
- Strong knowledge of statistics, optimization, probability theory, and experimental methodologies; proficiency in Python, R, or Java; experience with TensorFlow, PyTorch, or scikit-learn; and familiarity with cloud platforms and scalable computing resources.
- Strong analytical, problem-solving, and collaboration skills, plus fluent written and spoken English.
Preferred experience includes applying ML to fraud detection or risk modeling; building end-to-end ML systems and integrating models into applications at scale; developing APIs or backend services or working with distributed systems; processing large datasets; and using AI-assisted development tools such as Claude Code or Copilot.
The posting lists Cary, North Carolina; Austin, TX; and Charlotte, North Carolina, and mentions hybrid work opportunities. Applicants must be authorized to work for any U.S. employer; Q2 cannot sponsor or take over visa sponsorship. Listed benefits include flexible time off, career development and mentoring, health insurance, paid parental leave for eligible new parents, and volunteering and recognition programs.