Top Machine Learning Jobs in Chicago, IL

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Lead design, development, and production deployment of AI/ML solutions and conversational chatbot platforms. Own end-to-end implementation, MLOps/CI-CD, containerized deployments, solution architecture, security and governance, mentor engineers, and collaborate with product, enterprise architecture, and vendors to deliver scalable, monitored AI systems.

Lead applied AI/ML initiatives, design and deliver production-grade software, develop secure high-quality code, troubleshoot, automate remediation, evaluate vendor architectures, champion best practices across engineering, and drive adoption of AI/ML, NLP, LLMs and streaming technologies.

Design, build, and maintain scalable self-service data and ML platform tools and workflows. Mentor junior engineers, collaborate with cross-functional teams, implement monitoring/alerts, drive infrastructure improvements, integrate new technologies, and produce technical documentation to support reliable, scalable data products and pipelines.

Responsible for validating payment network models, including fraud and AML. Engage with stakeholders, improve model accuracy, and leverage innovative technology for data analysis.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

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

Advise state agencies on AI strategy and roadmaps, prioritize use cases, assess readiness and data maturity, define responsible AI governance, facilitate stakeholder workshops, and support early-stage implementation and adoption planning.

Analyze large datasets to build and compare statistical and machine learning models for fraud risk. Partner with Credit Risk, Product, and Engineering to develop fraud prevention strategies, monitoring, and alerts. Maintain KPIs, prepare dashboards and reports, present portfolio health to senior leaders, and take end-to-end ownership of fraud loss and decline rate performance.

As a Senior Data Scientist, you'll develop learning systems, algorithms, and models for optimizing marketing campaigns while collaborating with product development teams and engineering.

Lead and scale AI, ML, predictive analytics, and intelligent automation for Revenue Intelligence. Identify high-value use cases, build and deploy LLM and agentic AI solutions and production models (forecasting, propensity, recommendation, customer intelligence), drive automation, measure adoption and business value, and partner cross-functionally while managing vendors and delivery frameworks.

Assist in deploying, monitoring, and maintaining machine learning models and infrastructure. Support data curation, implement and test ML algorithms, help build CI/CD pipelines, automate training/deployment, troubleshoot production issues, and follow organizational security and safety practices while working under supervision.

Lead big-data analytics projects: acquire and consolidate diverse data, build and validate predictive and statistical models, analyze complex trends, present actionable insights, collaborate with partners, and ensure compliance of data practices.

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.

10-week in-person research internship partnering with Quant Researchers to explore research ideas, build analysis tools, learn options theory and market making, and develop skills for potential conversion to a full-time Graduate Researcher role.

Lead development and deployment of ML models for trading, drive research and prototyping, oversee data and feature engineering, mentor researchers, and build scalable ML research infrastructure.

Lead design and implementation of AI/ML models, data integration, analysis, and visualization for translational research. Enhance open-source data platforms (Gen3), enforce data governance, mentor junior staff, collaborate with researchers, and translate scientific requirements into production-ready data science solutions.

Lead definition, development, and delivery of AI-powered features across the myQ ecosystem. Own AI product roadmap, translate customer needs into requirements, partner with engineering and data science, manage backlogs, measure impact, and drive cross-functional launches to grow subscription revenue and product engagement.

Design, build, and deploy AI and machine learning solutions at scale. Develop data pipelines and scalable models, perform data wrangling and analysis, apply NLP and neural networks, collaborate with clients and teams, and contribute to open-source and iterative improvement of AI systems.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions on-site, collaborating with engineering teams to operationalize AI systems across multi-cloud platforms.

Lead development and operationalization of a clinical reasoning framework for trustworthy clinical AI. Partner with product, engineering, editorial, and design to define decision pathways, evaluation benchmarks, validation protocols, and ongoing expert review. Translate clinical feedback into AI system improvements, represent the team externally, and optionally maintain limited clinical practice.