Real-time Fraud ML Engineer: Scale & Impact

Q2; Cary (NC); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Hybrid Work

Flexible Time Off

Career Development

Health Benefits

Parental Leave

Volunteer Programs

Peer Recognition

Job summary

Q2, a leader in digital banking and lending, seeks a Machine Learning Engineer to build systems behind fraud detection at scale. You will work with data scientists and engineers to deploy and improve models affecting trillions of dollars in transactions annually.

You'll gain hands-on experience across model development, evaluation, deployment, and monitoring, solving real customer problems with rigor and reliability. This applied role emphasizes testability and impactful outcomes.

Qualifications

• Bachelor’s degree in a related field; 2+ years of relevant ML experience.
• Proven experience in ML model development and deployment.
• Strong knowledge of statistics, optimization, probability theory, and experimental methodologies.
• Proficiency in Python, R, or Java.
• Experience with ML frameworks (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.
• Build next-generation ML products across identity, behavior, and transaction fraud; partner with customers to shape product direction.
• Design and optimize real-time, low-latency ML infrastructure for reliability, scalability, and performance.
• Maintain pipelines for training, evaluation, and inference; collaborate with data scientists to productionalize models.
• Write clean, tested code following production engineering best practices and AI tooling.
• Support monitoring and troubleshooting of production ML systems and data pipelines.

Skills

ML model development

Python

R

Java

TensorFlow

PyTorch

scikit-learn

Cloud platforms

Statistics

Problem solving

Collaboration

Education

Bachelor’s degree

Job description

Q2, a leader in digital banking and lending, seeks a Machine Learning Engineer to build systems behind fraud detection at scale. You will work with data scientists and engineers to deploy and improve models affecting trillions of dollars in transactions annually.

You'll gain hands-on experience across model development, evaluation, deployment, and monitoring, solving real customer problems with rigor and reliability. This applied role emphasizes testability and impactful outcomes.