We are a leading provider of digital banking and lending solutions serving banks, credit unions, financial services organizations, and fintech companies across 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 Us Special?

We are just as passionate about our people as we are about our mission. We celebrate employees through recognition programs, company-wide celebrations, and opportunities to give back to the community. 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 believe strong collaboration comes from building genuine connections through team events, volunteer initiatives, and philanthropic programs.

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 manager.

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 decision-making

• Develop scalable machine learning pipelines and systems

• Stay current with emerging AI and machine learning trends and technologies

• Ensure the quality and performance of AI systems through testing and validation

Experience & Knowledge

• Bachelor’s degree in a related field with 5–8 years of relevant experience

• Proven experience in machine learning 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 machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn

• Familiarity with cloud platforms and scalable computing resources

• Strong analytical, problem-solving, communication, and collaboration skills