Real-Time Fraud ML Engineer (Hybrid)

Q2; Charlotte (NC); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid Work Opportunities

Health insurance

Parental leave

Career Development & Mentoring

Job summary

Q2, a leading provider of digital banking and lending solutions, is seeking a Machine Learning Engineer to build production fraud-detection systems at scale. You will collaborate with data scientists to turn models into reliable real-time applications.

This role offers hands-on exposure across model development, evaluation, deployment, and monitoring, with opportunities to impact fraud losses for financial institutions and their customers.

Qualifications

• 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.

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.

Skills

ML model development

Python

Cloud computing

Statistics

Education

Bachelor’s degree in related field

Tools

TensorFlow

PyTorch

scikit-learn

Job description

Q2, a leading provider of digital banking and lending solutions, is seeking a Machine Learning Engineer to build production fraud-detection systems at scale. You will collaborate with data scientists to turn models into reliable real-time applications.

This role offers hands-on exposure across model development, evaluation, deployment, and monitoring, with opportunities to impact fraud losses for financial institutions and their customers.