ML Engineer: Fraud Detection & Production ML

Q2; Austin (TX); On-site; USD 27,552,000 - 41,328,000; Part time

Job summary

Q2, a leading digital banking and lending solutions provider, seeks a machine learning intern in Austin, TX. You will work across the full ML lifecycle, from data pipelines to model evaluation, in a team that values learning and collaboration.

Requirements include progress toward a CS/DS/ML degree, Python experience, familiarity with ML frameworks, and a curious, analytical mindset. Nice-to-have: fraud detection projects, APIs, and large datasets.

Qualifications

• Currently pursuing a degree in Computer Science, Data Science, Machine Learning, or a related field.
• Coursework or project experience with Python (R or Java a plus).
• Exposure to ML frameworks or libraries such as TensorFlow, PyTorch, or scikit-learn.
• Foundational knowledge of statistics, probability, or experimental methods.
• Strong analytical thinking, curiosity, and a collaborative mindset.

Responsibilities

• Support research into emerging fraud and abuse patterns, and help translate findings into new detection ideas
• Help build and test features for ML products across identity, behavior, and transaction fraud
• Assist in building and maintaining pipelines that support training, evaluation, and inference of ML models
• Write clean, well-tested code alongside engineers, using modern AI-assisted development tools
• Help monitor and troubleshoot production ML systems, including data pipelines and model performance

Skills

Python

ML frameworks

Statistics

Analytical thinking

Education

Pursuing CS/DS/ML degree

Tools

TensorFlow

PyTorch

scikit-learn

Job description

Q2, a leading digital banking and lending solutions provider, seeks a machine learning intern in Austin, TX. You will work across the full ML lifecycle, from data pipelines to model evaluation, in a team that values learning and collaboration.

Requirements include progress toward a CS/DS/ML degree, Python experience, familiarity with ML frameworks, and a curious, analytical mindset. Nice-to-have: fraud detection projects, APIs, and large datasets.