Top Machine Learning Jobs in Chicago, IL
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.
As a machine learning engineer, you will work on a large-scale machine learning system, contribute to recommender systems, and continuously improve solutions.
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.
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This role directs the development, implementation, testing and maintenance of models used for margin, clearing fund and stress testing.
Lead Machine Learning Scientist position at Strong Analytics. Collaboratively design and develop machine-learning based solutions for clients. Lead project teams, work directly with clients, and bring state-of-the-art solutions to clients' problems.
Seeking Machine Learning Scientists with proficiency in Python and/or R, SQL, and experience in developing and implementing ML solutions. Responsibilities include building data analysis pipelines, training and validating machine learning models, and collaborating on research.
Build product features powered by machine learning on top of unique, proprietary datasets. Focus on NLP and large language models. Collaborate with product managers and engineers to prototype, build, and release features. Write production python code and deploy microservices.
As a machine learning engineer at Tegus, you will build product features powered by machine learning on top of unique datasets. Your work will involve NLP and large language models. You will collaborate with product managers and engineers to prototype, build, and release features. A bachelor's degree in a quantitative discipline or relevant experience is required.
Develop advanced algorithms, execute statistical and data mining techniques, evaluate emerging datasets and technologies, contribute to thought leadership, strong attention to detail and problem-solving skills.
Develop advanced algorithms, execute statistical and data mining techniques, evaluate emerging datasets, contribute to thought leadership, engineer features, and have expertise in machine learning algorithms.
Build, orchestrate, and monitor model pipelines, scale machine learning algorithms on massive data sets, implement ML Ops, write production-ready code, collaborate with client teams and global development team, research and evaluate latest technologies.
Design, build, and launch credit products and features for Cash App's Underwriting & Credit organization. Solve challenging technical problems at scale, collaborate with cross-functional teams, and mentor fellow engineers. Embrace changes in technology to promote future evolvability of tech stack.
Play a major role in building AI and Machine Learning solutions to enhance user experience and workflow efficiency for enterprise services. Collaborate with a team to develop intelligent software solutions using advanced technologies. Participate in data acquisition, requirements, and evaluation to ensure performance and quality at scale.
As an Applied Machine Learning engineer at Atlassian, you will work on the development and implementation of cutting-edge machine learning algorithms, training models, and collaborating with product, engineering, and analytics teams to integrate AI functionalities into Atlassian products and services. Responsibilities include designing system and model architectures, conducting rigorous experimentation, and applying AI/ML to improve product problems.
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