Top Machine Learning Jobs in Minneapolis, MN

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 an AI/ML Ops Engineer, you will architect and operate ML pipelines, automate CI/CD processes, and collaborate with cross-functional teams to enhance engineering productivity.

The role involves designing scalable AI solutions, managing large-scale AI deployments, building client relationships, and delivering presentations. Requires experience in MLOps and knowledge of ML ecosystems.

The Machine Learning Engineer will enhance search retrieval, train ranking models, build query understanding systems, and ensure robust pipelines for Reddit's AI-driven products.

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.

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.

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.

As a Software Engineer, you'll enhance the user experience of ML tools, optimize workflows, and collaborate with product managers on ML infrastructure projects.

As a Machine Learning Engineer, you will develop and deploy AI/ML solutions, collaborate with product managers and engineers, and improve learning experiences.

Lead the backend engineering team for Runway's machine learning platform, focusing on building infrastructure, monitoring, and scaling production processes.

The Senior Software Engineer will develop and maintain foundational ML infrastructure for feature computing, storage, and retrieval, impacting numerous teams at Reddit.

Develop ML models and pipelines for autonomous tractor initiative, improving system safety and performance in production environments.

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 an ML Engineer, you will build and deploy machine learning models for autonomous robots, collaborating with ML scientists and engineers to enhance AI capabilities.

The Senior Product Manager will lead the strategy and execution of AI-driven features in a SaaS platform, ensuring high-quality user experiences and data-driven decisions while mentoring junior team members.

Develop and optimize large-scale ML models and training pipelines for automating PCB layouts, collaborating on data pipeline design and novel architectures.

As a Senior ML Ops Engineer, you'll build ML infrastructure, implement automated deployment, optimize model serving, and ensure production performance.

Design and deploy machine learning models for autonomous vehicles, focusing on data tagging, dataset curation, and model validation.

The Engineering Manager - Data & ML will redesign and scale Underdog's data and ML platform, establishing best practices, leading a team, and collaborating with cross-functional teams to optimize data systems.

Lead the development of AI agents and infrastructure, collaborate with stakeholders, design APIs and SDKs, and advocate for best practices.

The Solutions Architect for AI/ML at Snowflake will advise clients on best practices for Data Science workloads, design solutions, and build ML pipelines. Responsibilities also include working hands-on with SQL and Python, maintaining technical expertise and collaborating with teams to improve products.

The Senior Machine Learning Engineer will develop scalable ML systems, lead a team, mentor juniors, and work cross-functionally to enhance customer experience through machine learning solutions.

The Practice Manager leads Data Science, AI, and ML projects, managing teams, fostering growth, and ensuring customer success through technical guidance and strategic business planning.

Lead the design, development, and implementation of machine learning products, creating data pipelines and maintaining production stability.