Top Machine Learning Jobs in Minneapolis, MN

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.

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.

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 Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

Senior ML Engineer on the Vision team responsible for framing ML problems, improving data acquisition and training pipelines, designing experiments, addressing model drift, building monitoring/visualization tools, collaborating across teams, and reviewing ML engineers' code to deploy robust models.

The role involves building ML infrastructure, creating data pipelines, ensuring data quality, and collaborating with data scientists to optimize models.

The Senior Software Engineer II at Aledade will develop scalable AI/ML solutions, mentor junior engineers, and collaborate with cross-functional teams in the healthcare industry.

Seeking a Senior Machine Learning Engineer to design and implement ML systems, optimize deployment, and collaborate with data teams, focusing on scalability and performance.

The Staff ML Platform Engineer will enhance Afresh's ML platform performance and scalability, collaborating with ML teams to improve systems and deploy models efficiently.

Drive delivery excellence and customer success by leading the architecture and implementation of AI/ML solutions while ensuring account growth and client engagement.

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.

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

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.