2027 Summer Internship - Machine Learning Engineer

Job description

## 2027 Summer Internship - Machine Learning EngineerApply: Cary, North Carolina: Full time: Posted Today: REQ-12800# **As passionate about our people as we are about our mission.** **Why Join Q2?**Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our mission is simple: build strong and diverse communities through innovative financial technology—and we do that by empowering our people to help create success for our customers. **What Makes Q2 Special?**Being as passionate about our people as we are about our mission. We celebrate our employees in many ways through our year-round Q2 ChangeMakers awards program and global moments of recognition and connection. We invest in the growth and development of our team members through ongoing learning opportunities, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun and giving back together. From company-wide volunteer days to events like our Q2 Homecoming Week—featuring learning, community service, and culture-building experiences—we create opportunities to connect, grow, and make an impact. ## **Projects You Could Work On**You'll get exposure across the full lifecycle of applied ML — from model development and evaluation to deployment, monitoring, and improvement — on software that solves real problems for real customers. Recent intern projects on this team and others have included enhancing our Iso Framework, building data pipelines for operational insights, and researching new approaches to emerging problems.## **What You'll Do*** 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## **What You'll Bring*** 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## **Nice to Have*** Coursework or personal projects involving fraud detection, risk modeling, or similar domains* Exposure to APIs, backend services, or working with large datasets* Comfort using AI-assisted development tools (e.g., Claude Code)*This position requires fluent written and oral communication in English.**Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.***Our Culture & Commitment:**We’re proud to foster a supportive, inclusive environment where career growth, collaboration, and wellness are prioritized. And our benefits go beyond healthcare—offering resources for physical, mental, and professional well-being. Click here to find out more about the benefits we offer. Q2 employees are encouraged to give back through volunteer work and nonprofit support through our Spark Program (see more). We believe in making an impact—in the industry and in the community.We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, or veteran status.*Applicants in California or Washington State may not be exempt from federal and state overtime requirements*

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Worksite address

cary, NC, 27518, US

Who can apply

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