Top Remote Machine Learning Jobs in Madison, WI

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.

As a Research Intern, you will work on AI/ML projects, contributing to the development of the Nebula LLM and applying machine learning techniques to real-world problems.

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

The ML Engineering Intern will contribute to the Nebula LLM development, tackle machine learning challenges, and gain hands-on experience in AI and language technology.

Senior ML research leader who translates state-of-the-art ML pipeline insights into product requirements, reference architectures, benchmarks, and platform roadmaps. Partners cross-functionally and with strategic customers and research institutions to set evaluation standards, reproducibility, and responsible ML practices while providing technical leadership and influencing product and executive audiences.

The role involves deploying, optimizing, and maintaining ML-driven motion planning and control algorithms for autonomous driving, collaborating across teams to ensure reliability under performance and safety constraints.

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 at Grailed will enhance personalization and recommendations, develop AI/ML solutions, and collaborate across departments for data-driven insights.

Intern will collaborate with data scientists and ML engineers to develop machine learning models and improve data pipelines for retail media insights.

As a Software Engineer in ML Infrastructure, you will develop scalable data pipelines, enhance data discovery, and collaborate with teams on data organization and ML features.

The Director of Machine Learning Science will lead teams in delivering ML solutions, drive innovation in search and personalization, and engage with engineering and product teams.

The Machine Learning Engineer will develop and maintain scalable ML systems, collaborate across teams, and innovate solutions for real-time applications to enhance customer experiences.

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

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

The Machine Learning Engineer will design and deploy computer vision models, build training pipelines, and enhance ML infrastructure to improve agricultural field operations.

The role involves designing and supporting GPU/Kubernetes clusters for AI/ML workloads, developing automation tools, and troubleshooting infrastructure issues.

As an AI/Machine Learning Intern, you will contribute to software applications and AI projects, working with data engineers and researchers to implement innovative solutions.

The intern will work on AI projects, gaining hands-on experience in software development, algorithm evaluation, and collaboration with engineers to enhance AI capabilities.

As a Senior Technical Recruiter for AI/ML, you'll manage full-cycle recruiting for senior AI roles, build talent pipelines, and partner with leadership to close top talent effectively.

As a Machine Learning Engineer, you will own the full lifecycle of ML from model development to production deployment, collaborating with teams to drive ML processes.

The role involves developing scalable ML solutions, leading cross-functional discussions, analyzing knowledge data, and producing technical documentation to improve search relevance using AI and machine learning technologies.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

The Senior Databricks AI/ML Engineer will design and deploy AI/ML solutions in the insurance sector, focusing on Databricks architecture, data pipelines, and MLOps workflows, while collaborating with various teams to operationalize models and optimize processes.

This role involves defining and managing the AI platform's roadmap, collaborating with cross-functional teams to integrate AI capabilities, and ensuring product quality and reliability metrics.