Machine Learning Engineer (Austin)

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, including our “Circle of Awesomeness” award ceremony and day of employee celebration among others! We invest in the growth and development of our team members through ongoing learning opportunities, mentorship programs, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun. We hold an annual Dodgeball for Charity event at our Q2 Stadium in Austin, inviting other local companies to play, and community organizations we support to raise money and awareness together.SUMMARY
The Machine Learning Engineer provides hands-on expertise in designing, implementing, and scaling AI solutions, while collaborating with cross-functional teams to advance machine learning capabilities and support business innovation.
Note: Specific tools, technologies, certifications, or travel requirements may be customized by the hiring managerRESPONSIBILITIES
• 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

EXPERIENCE AND KNOWLEDGE
• Bachelor’s degree in related field and 5–8 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 skillsThis 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.Health & WellnessHybrid Work OpportunitiesFlexible 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.Applicants in California or Washington State may not be exempt from federal and state overtime requirementsSummaryLocation: Austin, TXType: Full time