Backed by Battery.
About the role
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 they're looking for
- 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
More about this role
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.
You'll get exposure across the full lifecycle of applied ML — from model development and evaluation to deployment, monitoring, and...
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