Top Machine Learning Jobs in Chicago, IL

Design, build, and deploy machine learning systems for controlling quantum processors. Develop reinforcement learning and Bayesian methods for QEC calibration, and work cross-functionally with product and R&D teams.

Lead strategic storytelling for Enterprise AI by translating complex AI strategy and capabilities into clear, executive-facing narratives and assets. Create presentations, digital content, and change communications to drive adoption and scale across business units. Partner with product, engineering, and change teams to craft measurable, business-focused stories that enable executive alignment and enterprise-wide AI enablement.

Senior individual contributor responsible for leading pet/P&C insurance pricing model development (GLMs and ML), owning end-to-end projects, performing EDA and root-cause analysis, communicating results to stakeholders, and mentoring junior team members.

Design, train, and productionize underwriting ML models for real-time transaction decisions. Build feature pipelines and training datasets, run experiments, integrate models into batch/real-time systems, and monitor data and model health while collaborating with cross-functional teams.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

The Senior Manager for Machine Learning will lead the ML team, coordinate initiatives, and enhance product development in a collaborative environment.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

The ML Data Engineer develops and maintains machine learning pipelines and systems, collaborating with teams to optimize manufacturing processes through data-driven insights and predictive models.

Lead Applied AI strategy and execution across Employee Benefits and Operations, overseeing ML, generative and agentic AI, RAG, and predictive analytics lifecycles. Drive governance, model risk management, evaluation, deployment, cross-LOB prioritization, talent architecture, and regulatory readiness to improve enrollment, billing, service, and operational efficiency.

Lead architecture and technical strategy for GenAI and Amazon Connect implementations. Translate business requirements into scalable conversational AI solutions, design integrations across CRM/WFM/data layers, validate development outputs, support testing/UAT, and promote reuse, security, and performance across cloud-based GenAI and digital routing systems.

Lead a new team to define and deliver AI-powered operational solutions across finance, staffing, delivery, and sales. Set strategy, scope problems, build scalable LLM-based tools, ensure adoption and maintenance, report measurable outcomes to the COO, and manage a distributed team focused on production-grade AI solutions.

The AVP Applied AI leads data science and AI capabilities at The Hartford, managing complex AI portfolios and ensuring delivery of business value through scalable models and AI-driven systems. Responsibilities include overseeing team performance, technical direction, AI governance, and alignment with business outcomes, along with collaboration across departments and continuous improvement initiatives.

Lead design and implementation of a scalable data lakehouse and analytical layer in Google Cloud, develop and deploy machine learning models and pipelines, mentor data scientists, set technical strategy, and partner with stakeholders to drive predictive formulation and product innovation.

Design and research AI-powered user experiences by identifying AI opportunities, creating intuitive human-AI interactions, collaborating with product and engineering, developing ethical design patterns, prototyping AI features, and evaluating usability, transparency, and trustworthiness of AI-driven systems.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

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

The Director of Analytics Consulting will lead analytics projects for CPG clients, translating business problems into high-level solutions and managing global teams, with a strong emphasis on data-driven insights and stakeholder engagement.

Lead presales for data and AI initiatives: design and present Microsoft Fabric-based data platforms, run discovery and AI workshops, scope proposals, translate technical designs into business value, mentor delivery, and support go-to-market and thought leadership.

Manage design and production deployment of AI/ML/LLM solutions; lead and mentor an AI Ops team; build and maintain CI/CD pipelines and integration code; ensure scalable, resilient, secure model deployments; partner with data science, DevOps, infrastructure, and release teams; enforce compliance, security, and governance for AI solutions.

Design, build, and ship production AI agents integrated end-to-end into a TypeScript/Node.js/React stack. Apply ML judgment to product decisions, improve agent reliability and evaluations, and move quickly in a startup environment owning ambiguous problems.

Lead design and delivery of a unified, real-time billing platform that embeds AI/ML to automate reconciliations, detect anomalies, enable predictive insights, and create autonomous finance workflows across global billing operations.

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.

Develop and operationalize ML and deep learning solutions (especially NLP/LLMs) for Wholesale Lending. Design experiments, build batch and real-time prediction pipelines and end-to-end AutoML/AutoNLP workflows, collaborate with product, data, engineering, legal and compliance teams, and deploy models to production on AWS.