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About the Role
As a Machine Learning Engineer on the Risk & Fraud team, you will build and operate the production systems behind fraud detection at scale. You will work closely with data scientists and engineers to turn models into reliable, real-time systems and continuously improve their performance in production, protecting nearly two trillion dollars in transactions for millions of users each year.
Responsibilities
- Research emerging fraud and abuse patterns and translate that research into new detection approaches.
- Help build next-generation ML products across identity, behavior, and transaction fraud, partnering directly with customers to understand their needs and shape product direction.
- Build and optimize real-time, low-latency ML infrastructure, continually improving its reliability, scalability, and performance.
- Build and maintain systems and pipelines that support training, evaluation, and inference for machine learning models, collaborating with data scientists to productionalize models into scalable applications.
- Write clean, maintainable, and well-tested code, following production engineering best practices and leveraging the latest AI tooling.
- Support monitoring and troubleshooting of production ML systems, including data pipelines and model performance.
Requirements
- Bachelor’s degree in related field and 2+ years of relevant experience.
- Proven experience in ML model development and deployment.
- Strong knowledge of statistics, optimization, probability theory, and experimental methodologies.
- Proficiency in programming languages such as Python, R, or Java.
- Experience with ML frameworks/libraries (TensorFlow, PyTorch, scikit-learn).
- Familiarity with cloud platforms and scalable computing resources.
- Strong analytical, problem-solving, and collaboration skills.
- Fluent written and oral communication in English.
- Authorized to work for any employer in the U.S. Sponsorship is not available.
Skills
- Machine learning
- Fraud detection
- Risk modeling
- Data pipelines
- Model training
- Model deployment
- API development
- Backend services
- Distributed systems
- Large datasets
- Data processing frameworks
- AI-assisted development tools
Location
- U.S.
Work Type
- Hybrid
Experience Level
- 2+ years of relevant experience
Education Level
- Bachelor’s degree in related field
Benefits
- Health & Wellness
- Hybrid Work Opportunities
- Flexible Time Off
- Career Development & Mentoring Programs
- Competitive health insurance offerings
- Generous paid parental leave for eligible new parents
- Community Volunteering & Company Philanthropy Programs
- Employee Peer Recognition Programs
About the Company
- 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.
- 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 havinging 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.
- We’re proud to foster a supportive, inclusive environment where career growth, collaboration, and wellness are prioritized.
- Our benefits go beyond healthcare—offering resources for physical, mental, and professional well-being.
- Q2 employees are encouraged to give back through volunteer work and nonprofit support through our Spark Program.
- We believe in making an impact—in the industry and in the community.
Equal Opportunity
- 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.