Top AI & Machine Learning Jobs in Chicago, IL

Lead a data science team developing scalable AI and NLP solutions for customer-facing financial applications. Partner with data scientists, engineers, product managers, and designers to build, train, evaluate, fine-tune, and operationalize machine learning and large language models. Drive research into generative AI, training optimization, self-supervised learning, explainability, and RLHF while delivering production systems at scale. Translate technical findings into business outcomes and develop team talent.

Lead the design, development, deployment, and operation of machine learning systems at scale. Responsibilities include building and validating models, creating optimized data pipelines, automating testing and deployment, monitoring and retraining production models, designing cloud-based ML architectures, and applying responsible AI, governance, security, and explainability practices. The role also involves technical leadership, cross-functional Agile collaboration, and mentoring or leading teams developing production ML solutions.

Leads product strategy and a product management team for enterprise AI/ML and machine learning platforms. Defines Generative AI and ML platform vision, evolves capabilities for data scientists and software developers, drives platform adoption, and partners with adjacent platform leaders on an integrated ecosystem. The role requires deep technical expertise in AI/ML infrastructure, Generative and Agentic AI, cloud and open-source technologies, people leadership, and AI risk management.

Leads the design, development, deployment, and maintenance of production machine learning systems at scale. Responsibilities include developing and validating models, building optimized data pipelines, writing application code, automating testing and deployment, monitoring and retraining production models, designing cloud-based ML architectures, and applying responsible AI and governance practices. The role collaborates with Product, Data Science, and Agile engineering teams and may include people leadership.

The Senior Manager, Data Science will lead a team to assess and mitigate model risks, develop machine learning models, and enhance decision-making processes across various business domains in Capital One's Model Risk Office.

The role involves leveraging technologies like Python and AWS to develop statistical and machine learning models, translating complex data insights into business goals, and working with a cross-functional team.

Build and deploy machine learning and AI models for the Global Payments Network. Partner with cross-functional teams to design, train, evaluate, validate, and implement models using cloud and open-source tools. Drive innovation, translate technical work to business impact, and mentor talent.

Lead a product team building ML and GenAI infrastructure and model delivery for marketing and acquisitions. Define problem statements, set 3-year roadmaps tied to business outcomes, partner with data scientists and engineers, drive reusable scalable capabilities, influence cross-functional stakeholders, recruit and mentor talent, and measure success with data-driven metrics.

Lead product efforts for machine learning-driven marketing and acquisition products. Collaborate with data scientists, engineers, and designers to define acceptance criteria, break down epics into MVP features and user stories, prioritize roadmaps, manage agile delivery, and drive stakeholder alignment to build scalable ML product platforms.

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.