Top Hybrid Machine Learning Engineer Jobs in Chicago, IL

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.

Develop advanced algorithms, apply machine learning, collaborate with clients, and assess new datasets and technologies for data science projects.

In this role, you will develop machine learning models for Anti-Money Laundering using advanced analytics and work with cross-functional teams.

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.

Design, build, and deploy scalable AI and GenAI solutions by wrangling data, developing ML models, and maintaining data infrastructure. Collaborate with clients, perform complex analyses, apply NLP and deep learning techniques, and mentor junior team members to deliver production-ready AI systems.

Conduct ML and deep learning research to build, test, and deploy predictive models for trading. Collaborate with traders, researchers, and engineers to engineer features, preprocess data, prototype algorithms, and improve production research tooling to enhance trading performance.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Collaborate with customers to implement data solutions, refine ML models, and contribute to product strategy while ensuring successful adoption and outcomes.

The role involves designing and deploying strategies for congestion revenue rights, applying quantitative methods, and developing machine learning models.