Top Senior Level Machine Learning Jobs in Chicago, IL

Develop and maintain the vision, roadmap, and strategy for the research and development product within the Decisioning Platform at Discover. Collaborate with stakeholders to align product features with business goals and lead the design and implementation of new product strategies. Define KPIs and OKRs, create prototypes based on customer feedback, and oversee product lifecycle activities.

As a Senior Machine Learning Engineer at Grainger, you will play a central role in evolving and expanding machine learning infrastructures to enhance the customer journey. Your responsibilities include deploying and maintaining ML systems at scale, designing data pipelines, and collaborating with data scientists to deliver business impact through ML products.

Join Arity's Geospatial Data Science team to build scalable distributed machine learning models and extract innovative insights from raw GPS data. Help make transportation smarter, safer, and more useful for everyone by creating actionable insights from geospatial data.

The Senior Engineering Manager will lead a team of engineers to build Dropbox's AI offering, Dash. They will drive product monetization, foster operational excellence, and provide strategic direction for scaling.

Manager of Machine Learning responsible for leading a team of data scientists and ML engineers to create new data products from financial transactions at Apex Fintech Solutions. Key duties include overseeing model development, collaborating with stakeholders, and keeping up with advancements in ML and AI. Requires 5+ years of building ML solutions, 3+ years of managing people in tech companies, and experience with cloud technologies like GCP, AWS, and Azure.

As a Senior Machine Learning Engineer at Movable Ink, you will be responsible for advancing the core machine learning solution and infrastructure, building scalable models, and deploying them to production. You will work on recommender-system related challenges, collaborate with data engineers and platform engineers, and continuously improve the ML platform.

Join the Data Science and Analytics Engineering team to build ML/NLP models and contribute to the automation of data processing. Require expertise in Machine Learning, deep learning, NLP, feature engineering, and optimization. Must have experience deploying ML models to production using AWS, GCP, or Azure.