Top Software Machine Learning Engineer Jobs in Chicago, IL

As Machine Learning Product Director, define ML strategy and manage product development for Attrition Risk Insights, enhancing customer engagement through predictive modeling.

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

The role involves defining and building the company's machine learning capabilities, designing production ML systems, owning data strategy, and partnering with engineers. Requires experience in ML system production and software engineering fundamentals.

Develop and maintain predictive models for quality, risk adjustment, and operational efficiency; analyze multi-source healthcare data; implement ML solutions, monitor model performance, and experiment with emerging AI to improve outcomes and workflows.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Design, prototype, and deploy movement and running-dynamics algorithms using wearable sensor data. Translate raw multi-axis sensor signals into production-grade movement metrics, collaborate with hardware and product teams, and validate algorithms for athlete-focused insights.

The Senior Machine Learning Engineer will develop AI products, support feature implementation, and troubleshoot ML services to enhance customer experiences.

Lead the Applied Machine Learning team at ExtraHop to develop systems for behavioral detection in network security. Manage model design, evaluation, and operationalization while maintaining experimental rigor and collaborating with product teams.

The role involves applying AI/ML techniques to develop model systems for healthcare, collaborating across teams, and conducting R&D.

Lead the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.