Top Fintech Machine Learning Engineer Jobs in Chicago, IL

Lead a team to analyze data, create machine learning models, and deliver insights for customer-focused financial products, leveraging various technologies.

This internship focuses on researching low-latency ML inference and hardware acceleration, involving project ownership, collaboration with engineers, and presenting findings.

This role involves developing ML tools and frameworks, supporting data science environments, and leading projects from design to execution. Candidates need strong programming skills and a quantitative background.

Build and deploy transformer and sequential deep learning models using large-scale financial and behavioral data to power personalized growth and marketing experiences. Work cross-functionally to design scalable training, serving, and monitoring infrastructure (batch and real-time), contribute to experimentation and optimization frameworks, and maintain production-grade MLOps systems to improve member engagement and business metrics.

As an Applied Machine Learning Engineer, you will develop AI and ML models to improve healthcare outcomes. Collaborate with teams to implement and optimize machine learning techniques and algorithms.

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

Build and operate Coinbase's LLM and agent platform: unified model gateway, AI Hub, secure microVM agent runtime, knowledge base integrations, AI FinOps, observability and evaluation tooling, and production-grade agent deployments with teams across the company.

Lead the SDL and Cash Line ML teams, developing innovative machine learning models to improve lending solutions and expand credit access. Collaborate with cross-functional teams to drive product strategy and enhance model performance, while mentoring a team of ML scientists.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.