Top Fintech Machine Learning Engineer Jobs in Chicago, IL

The Staff AI Engineer will design, implement, and maintain machine learning algorithms and systems, integrating AI components into applications and collaborating with cross-functional teams using cloud technologies and DevOps principles.

Lead the development of statistical and machine learning solutions for actuarial pricing and reserving, collaborating with various stakeholders and managing model lifecycles.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

Design, develop, and deploy production machine learning models to improve decisioning for consumer lending. Partner with business and technical teams to develop data sources, improve modeling methodology, manage models in production, and mentor others while practicing strong software engineering and MLOps practices.

The Senior Machine Learning Engineer will build and operate ML systems end-to-end, focusing on production-grade ML platforms, feature pipelines, and MLOps practices while collaborating with cross-functional teams.

Lead development and refinement of machine learning models for pricing and profitability. Collaborate with various teams, synthesize insights, and improve business outcomes while adhering to agile methodologies.

As an Applied Scientist, you will design and implement machine learning models, develop personalized systems, and enhance decision-making through AI technology to improve user experience and business operations.

Lead a team to develop machine learning models for credit strategies, collaborating across functions and delivering measurable business outcomes.

The Senior Machine Learning Engineer will manage the lifecycle of ML models, transform prototypes to production code, build training pipelines, and monitor system performance.

Lead development and lifecycle of yield-optimization predictive models, integrate models into the recommendation engine, improve model accuracy to drive revenue and conversion, match users to optimal offers, and manage external partner relationships while collaborating across engineering and business teams.