Top Machine Learning Jobs in Minneapolis, MN

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

The role involves designing and building scalable data platforms for analytics and AI, optimizing data pipelines, and supporting machine learning workflows. Collaboration with stakeholders to define technical solutions and drive initiatives for data quality and observability is key.

Design, build, and deploy enterprise-scale generative AI and LLM applications, agentic workflows, and RAG pipelines. Develop full-stack solutions using React/Next.js, Python/Node.js, and PostgreSQL; integrate vector databases and optimize retrieval; lead technical design, mentor engineers, and ensure responsible AI deployment and scalability.

The Lead AI/ML Engineer will design and implement ML models, integrate AI solutions, and collaborate with technical teams to enhance AI capabilities, focusing on healthcare applications.

As a Manufacturing Automation Engineer IV, you will lead the design and implementation of automated manufacturing systems, optimize processes, and collaborate with teams to enhance efficiency and quality.

As a Manufacturing Automation Engineer V, you will lead automation projects, design production lines, and optimize processes to enhance safety and efficiency.

The Enterprise Account Executive will manage the complete sales cycle, build relationships with senior leaders, and drive platform adoption within data-intensive organizations.

Lead design and delivery of scalable AI/ML solutions: translate problems into models, build and deploy ML/agentic architectures and conversational AI, integrate cloud AI/cognitive services, create specs and test plans, manage projects and stakeholder collaboration to productionize models and optimize performance.

Design, develop, and test next-generation immunoassay and clinical chemistry analyzers. Work with fluidics, pneumatics, motors, sensors, robotics, ML/vision, collaborate cross-functionally, investigate field issues, and participate in test development and risk reduction.

Design, develop, and test next-generation immunoassay and clinical chemistry analyzers. Collaborate with cross-functional teams to define and verify system requirements, troubleshoot field issues, develop test methods, and improve usability and reliability of software-controlled diagnostic instruments.

Design, develop, and test next-generation immunoassay and clinical chemistry analyzers. Collaborate cross-functionally on system requirements, investigate customer-site issues, develop test methods, and support usability and reliability improvements.

Develop AI approaches for healthcare, build and test ML models, conduct experiments, analyze data, and collaborate on solutions.

The Level 3 Support Analyst - AI Specialist resolves complex technical issues for customers, focusing on AI solutions, troubleshooting, and collaborating with multiple teams. Responsibilities include investigating issues, mentoring junior staff, and providing 24/7 emergency support.

Design, develop, deploy, and maintain scalable detections across SIEM, EDR, cloud, identity, and network platforms. Translate threat intelligence and incident findings into high-fidelity detection logic, validate coverage against MITRE ATT&CK, measure detection performance, and collaborate with intelligence, IR, hunting, and platform teams to close visibility gaps and automate detection workflows.

Design, develop, and deploy Generative AI and ML solutions (LLMs, RAG, agentic frameworks) for healthcare claims adjudication. Build end-to-end pipelines, apply MLOps, ensure LLM security and responsible AI, deploy on cloud platforms, integrate data warehousing, collaborate with product and platform teams, and mentor junior engineers.

The Sr Engineer in Cybersecurity AI Activation and Engineering designs and implements AI-driven solutions, works with cybersecurity processes, and keeps up with emerging technologies.

The Analytics Lead will leverage data analytics to optimize credit strategies, monitor portfolio performance, and collaborate on credit policy with stakeholders across technical and non-technical teams.

Lead the design, development, and deployment of AI and machine learning solutions, mentor teams, and drive innovations in healthcare data analysis.

As a Senior AI/ML Engineer, you will design and build end-to-end ML systems, manage ML lifecycle workflows, and ensure compliance with AI standards within the healthcare sector.

Design and implement AI/ML solutions for healthcare, collaborating with teams to translate complex requirements, deploy models, and process large datasets.

Lead end-to-end design, implementation, deployment, and operation of production AI/ML-enabled full-stack applications. Build LLM-powered features, design scalable backend services and data models, deploy on Azure, implement CI/CD and observability, and mentor engineers while ensuring secure, compliant integration into clinical workflows.

Develop and evaluate novel AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, document models, collaborate with stakeholders, RUAI, and engineering to productionize solutions and MLOps pipelines, and publish/patent research.

Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.

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

Lead ML feature strategy and engineering for fraud detection, owning end-to-end pipelines, validating features at scale, and translating analytics into production ML systems. Drive tooling and architecture decisions, mentor junior team members, partner cross-functionally with product and risk teams, and apply practitioner-level ML knowledge to detect and prevent account takeover and scam activity.