Top Machine Learning Jobs in Minneapolis, MN

The Solutions Architect will lead the architecture and design of IoT solutions, defining technical strategies and collaborating with teams to ensure scalable and secure systems.

Own and scale Coinbase's notification platform and experience to grow MAU, drive engagement, and maximize LTV using ML-driven personalization, analytics, and cross-functional execution.

Design and implement the core Risk platform architecture, manage projects, mentor team members, and write high quality code for Coinbase products.

Lead delivery of AI-powered audit products: define vision and roadmap, engage customers, prioritize backlogs, collaborate with Design/Engineering/AI Labs, use metrics to drive product discovery and delivery, and automate connected audit workflows.

The Senior Applied Scientist will develop and manage AI solutions for Thomson Reuters, focusing on machine learning, NLP, and information retrieval to drive product innovation. They will collaborate with product teams and stakeholders, ensuring technical alignment with business goals.

The Senior Research Engineer will develop and deliver scalable ML solutions, collaborate with teams, and apply innovative technologies in AI to impact professionals in legal and accounting fields.

As a Lead Applied Scientist, you will define research strategy, mentor teams, and innovate AI/ML solutions for legal and other professional sectors.

The Client Partner will drive adoption of the Aera Decision Cloud by engaging with senior executives at enterprise clients, identifying opportunities, and managing client relationships. Responsibilities include strategic planning and achieving revenue targets in a consultative sales approach.

The AI Engagement Manager will foster relationships with top partners, facilitate adoption of AI solutions, and analyze performance using BI tools.

Lead the product vision, strategy, and execution for Consumer Risk within Checkout at Affirm, focusing on credit decisioning, pricing, and fraud prevention while managing a team of Product Managers and collaborating with various functional teams.

This role involves leading and coordinating technology initiatives in the Spend Management domain, collaborating with cross-functional teams to ensure effective program execution, risk management, and alignment with responsible AI practices.

As Principal Product Manager for the Agentic Platform, you will set the vision and strategy for AI infrastructure, drive product development, and facilitate adoption across teams.

Lead research on large language models to address healthcare challenges, collaborating with teams to enhance model performance and ensure real-world applicability.

As a Machine Learning Engineer, you will perform analysis on healthcare data, develop and deploy clinical machine learning models, and collaborate with various stakeholders.

Design, develop, and deliver machine learning models for clinical data. Collaborate with stakeholders and optimize production ML systems.

Lead development of a greenfield data product by partnering with customers and product teams to build analytics and models (simulation, Bayesian networks, optimization, ML). Maintain existing analytics, improve predictive churn models, and translate customer needs into production-grade SQL/dbt pipelines, dashboards, and data-driven product features.

The Senior Engineering Manager will lead multiple growth engineering teams to build products, improve engineering quality, and drive significant business impact, focusing on user growth through innovative strategies while ensuring high-quality execution under deadlines.

Lead AI/ML projects focusing on health applications, design and scale AI systems, and collaborate across teams to improve solutions using advanced machine learning methodologies.

As a Senior Machine Learning Engineer, you'll design and manage ML infrastructure, work with Data Science to implement models, and oversee system health and reliability.

Build and optimize machine learning models for 3D dental applications, research state-of-the-art methods, and implement MLOps best practices.

Lead the development and implementation of automation techniques using AI and machine learning to improve life underwriting operations and processes.

Design, build, and deploy generative AI solutions (LLMs, RAG, agentic systems) using Python and .NET/C#. Develop APIs, containerize models, implement CI/CD, collaborate with stakeholders, and support production systems.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

As a Data Scientist, you will design and deliver complex predictive models, communicate insights, and mentor less experienced talent while solving key business challenges using advanced statistical techniques and machine learning.