Staff Machine Learning Engineer
Confirmed on the employer's own hiring board on Oct 15, 2026. First seen by Alion on Aug 14, 2026.
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.
SUMMARY
The Staff Machine Learning Engineer is a technical leader responsible for designing, building, and scaling production-grade AI systems that drive measurable business impact. This role goes beyond model development to owning end-to-end AI solutions-from problem definition through deployment, adoption, and continuous improvement.
Operating within the AI Producer model, this role partners with Product, Engineering, and Delivery teams to translate high-value opportunities into scalable AI capabilities, including agentic workflows and GenAI-powered systems.
RESPONSIBILITIES- Design and implement machine learning algorithms and models for various business applications
- Conduct research and experimentation to advance machine learning capabilities
- Collaborate with cross-functional teams to integrate AI solutions into production environments
- Analyze large datasets to extract meaningful insights and support data-driven decisions
- Develop scalable machine learning pipelines and systems
- Maintain up-to-date knowledge of emerging AI and machine learning trends
- Ensure the quality and performance of AI systems through testing and validation
- Provide technical guidance and mentoring to junior engineers
EXPERIENCE AND KNOWLEDGE
- Bachelor’s degree in related field and 9-12 years 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
This position requires fluent written and oral communication in English.
Health & Wellness
Hybrid Work Opportunities
Flexible Time Off
Career Development & Mentoring Programs
Health & Wellness Benefits, including competitive health insurance offerings and generous paid parental leave for eligible new parents
Community Volunteering & Company Philanthropy Programs
Employee Peer Recognition Programs - “You Earned it”
Click here to find out more about the benefits we offer.
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.