Top Senior Machine Learning Engineer Jobs in Chicago, IL

Lead and collaborate on engineering design and development, write maintainable code, and partner with clients to extract requirements and manage projects.

The Senior Machine Learning Engineer will build and deploy ML models for Risk and Fraud detection, lead technical efforts, manipulate data using SQL, develop code, and partner with cross-functional teams.

The Senior/Staff Machine Learning Engineer will design scalable data pipelines, implement ML algorithms, and work on microservice architectures for ad optimization.

Develop LLM solutions, optimize ML pipelines, mentor teams, present at conferences, and collaborate with internal teams to shape ML initiatives.

The Lead Machine Learning Engineer will design and implement ML applications and systems, mentor junior developers, and optimize data pipelines while collaborating with cross-functional teams.

As a Senior Machine Learning Engineer, you will focus on the development and maintenance of machine learning infrastructure, deploying scalable models, collaborating with teams, and mentoring junior engineers.

Design and develop machine learning models, oversee project execution, mentor teams, and maintain client relationships while driving innovative solutions and improvements.

The Staff Machine Learning Engineer leads the development of AI and machine learning systems, mentors teams, and ensures scalable, production-grade solutions while enhancing user engagement.

The role involves building and maintaining an ML platform for training and deploying ML models, enhancing their performance and reliability.

Develop specialized software for real-world machine learning use cases, optimize system performance, and manage production environment health.

Join the Hugging Face Special Ops team to bridge AI models with hardware, design user-friendly APIs, and enhance performance and deployment efficiency.

As a Machine Learning Engineer at Hightouch, you will build intelligent solutions for data activation, focusing on personalization, experimentation, predictive modeling, content generation, and budget optimization. Responsibilities include customer research, problem definition, and machine learning infrastructure development.

This role involves building and deploying machine learning models for Risk and Fraud, leading technical efforts, and collaborating with operations teams.