ML Engineer – Fraud Detection & Real-Time Systems

q2ebanking; Cary (NC); On-site; USD 110,000 - 160,000; Full time

Job summary

Q2 is seeking a Machine Learning Engineer to join the Risk & Fraud team in the United States. You will build and operate production systems behind fraud detection at scale, protecting trillions in transactions annually and turning models into reliable, real-time services.

You will collaborate with data scientists and engineers, advancing model development from training to deployment and monitoring, with emphasis on performance, latency, and reliability.

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.
• Strong analytical, problem-solving, and collaboration skills.

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

Python

R

Java

Statistics

ML concepts

Education

Bachelor's degree in related field

Tools

TensorFlow

PyTorch

scikit-learn

Job description

Q2 is seeking a Machine Learning Engineer to join the Risk & Fraud team in the United States. You will build and operate production systems behind fraud detection at scale, protecting trillions in transactions annually and turning models into reliable, real-time services.

You will collaborate with data scientists and engineers, advancing model development from training to deployment and monitoring, with emphasis on performance, latency, and reliability.