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.
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 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
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