About the role#
Q2 is hiring a Machine Learning Engineer Intern for the Summer 2027 term. This is a full-time, on-site position based in Austin, Texas. You will work across the full lifecycle of applied machine learning, from initial model development to deployment and monitoring, to help solve real-world problems in the financial services industry.
What you'll do#
- Research emerging fraud and abuse patterns and translate your findings into new detection ideas.
- Build and test features for ML products focused on identity, behavior, and transaction fraud.
- Assist in the construction and maintenance of pipelines for model training, evaluation, and inference.
- Write clean, well-tested code in collaboration with our engineering team.
- Use modern AI-assisted development tools to support your workflow.
- Monitor and troubleshoot production ML systems, including data pipelines and model performance metrics.
What you'll need#
- Current enrollment in a degree program for Computer Science, Data Science, Machine Learning, or a related field.
- Coursework or project experience using Python, with R or Java as a plus.
- Exposure to ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Foundational knowledge of statistics, probability, or experimental methods.
- Strong analytical thinking, curiosity, and a collaborative mindset.
- Fluent written and oral communication in English.
- Authorization to work for any employer in the U.S. without sponsorship.
- Preferred: Coursework or projects involving fraud detection or risk modeling.
- Preferred: Experience with APIs, backend services, or large datasets.
- Preferred: Comfort using AI-assisted development tools.
Location & details#
- Location: Austin, Texas (On-site).
- Term: Summer 2027.
- Status: Paid, full-time internship.
About Q2
Q2 provides digital banking and lending technology for banks, credit unions, and fintech firms. The company builds software for consumer, small business, and corporate account holders. Based in Austin, Texas, it operates internationally with a workforce of 1,001 to 5,000 employees. It focuses on security and data-driven financial experiences.
How to get in at Q2
Applying early at Q2 gives you a distinct advantage before the applicant pool becomes unmanageable. Intern Insider sends an instant alert the moment a role matching your target is published, ensuring you are among the first to submit your materials. This timing helps your application get seen before the pile grows. You can also improve your response rates by reaching out to the recruiters behind these roles directly. Intern Insider surfaces the specific contacts for Q2 positions, allowing you to ask informed questions or request a referral. This direct approach often proves more effective than waiting for a response from a general queue.
