Top Remote Machine Learning Engineer Jobs in Chicago, IL

Design and deliver customer experience solutions using machine learning techniques, analyze customer data, implement ML algorithms, and automate processes.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

Design and maintain scalable machine learning infrastructure, collaborating with data scientists, ensuring performance, security, and compliance of AI systems.

As a Senior Staff Data Scientist/Machine Learning Engineer, you will develop machine learning models to enhance Autodesk's eCommerce platforms, drive data-informed decisions, and mentor junior team members.

Work on Wayfinding projects using LLMs and agentic workflows, collaborate with engineering/product/research teams, receive mentorship, and gain hands-on experience with model evaluation, prompt engineering, and shipping code in a real engineering environment.

As a Machine Learning Engineer at Shipwell, you'll build and scale AI-powered logistics solutions, maintaining data pipelines and ML infrastructure across the organization.

Join a team focusing on end-to-end ML Ops for the insurance sector. Develop and deploy ML solutions, build infrastructure, and solve business challenges using advanced technologies.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

The role involves building AI technologies for customer service, developing ML systems, and collaborating with cross-functional teams to enhance user experiences.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

The Machine Learning Engineer will work on AI/ML projects, optimizing data systems, supporting cloud migrations, and delivering predictive insights using AWS services.

The AI/Machine Learning Engineer will develop systems for automating health benefits decisions, integrating large language models with rule-driven patterns, and ensuring compliance with regulations.

Lead the advancement of embedding-based retrieval systems, focusing on multimodal models, model performance, and extensibility for various use cases.

As a Staff Machine Learning Engineer, you will design scalable ML systems, mentor engineers, solve complex problems, and optimize models in a fintech environment.

As a Machine Learning Engineer, you'll innovate features for dataset curation, model analysis, and integrations, focusing on Visual AI and open-source contributions.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.

The ML Engineer will work on RL frameworks, develop synthetic data engines, and maintain performance evaluations for a closed-loop drug discovery platform.

Design, architect, and deploy generative AI/LLM solutions from prototype to production; work directly with clients to translate business needs into technical plans; write production-quality code integrating LLMs and ML models; collaborate with cloud, DevOps, and frontend teams; support proposals, sales, and evaluation/observability of models.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

As a Senior Machine Learning Engineer, you will design ML infrastructure, lead zero-to-one initiatives, collaborate on projects, and enhance scalability in the development of ML applications.

As a Staff Machine Learning Engineer, optimize card pricing models, lead ML lifecycle, collaborate with experts, and enhance predictive features.

As a Senior Staff Machine Learning Engineer, you'll lead projects on ML models for search and recommendations, impacting over 150M users. Responsibilities include improving ML systems, collaborating with cross-functional teams, and developing infrastructure for model deployment.

The role involves building ML systems, improving models across product surfaces, and leveraging data for personalization and user engagement.

As a ML Engineer focused on inference, you'll design and optimize deep learning models for real-time communication AI applications, collaborating with cross-functional teams and ensuring production readiness.

Lead the ML and AI engineering team, strategizing and implementing AI solutions for business growth, mentoring staff, and collaborating across departments.