Top Machine Learning Jobs in Minneapolis, MN

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead design and delivery of scalable AI/ML solutions: translate problems into models, build and deploy ML/deep learning and NLP systems, design agent and multi-agent architectures, collaborate with stakeholders, create specs and test plans, and manage project delivery and production model integration.

Design, build, and productionize AI systems (genAI, agents, RAG) with observability, model quality, governance, and responsible-AI controls. Implement serving/inference, cloud deployments, model registry/experiment tracking and cross-functional leadership to scale claim-edit automation and agentic workflows.

Lead a full-stack engineering team building, deploying, and operating production-grade AI/ML applications (LLMs, deep learning, agentic systems). Drive technical architecture, infrastructure, data, APIs, reliability, security, and ML Ops. Coach and develop engineers, align stakeholders, manage roadmaps, and ensure scalable, maintainable solutions that deliver measurable business value.

Combine customer-facing supply chain consulting with internal analytics and AI tools: assess customer operations, design logistics solutions, build dashboards and predictive models, automate workflows, integrate systems, support sales/RFPs, and drive continuous improvement across TMS/WMS/BI platforms.

Lead product strategy and execution for an AgTech SaaS platform, driving grower workflows, data integration, analytics, IoT partnerships, and commercialization. Own roadmap, write requirements, collaborate with engineering/data science, and measure adoption, revenue retention, and platform usage.

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.

Lead enterprise robotics strategy and architect reusable AMR, cobot, and mobile manipulator platforms for global manufacturing. Define roadmaps, standards, and governance; oversee integration of perception, motion planning, fleet management, safety, IT/OT, and validation. Drive prototyping through factory validation and deployment, mentor engineers, manage supplier/system integrator relationships, establish an automation lab, and influence technology investments to scale robotics across sites.

Lead and grow an AI/ML engineering team to design, build, and deploy production-grade ML/AI solutions (NLP, NLU, deep learning, CV, ASR). Oversee architecture, MLOps, experiments, model monitoring/versioning, cross-functional collaboration, and ethical AI practices to translate research into scalable business solutions.

Design, develop, and deploy agentic AI workflows and LLM-based automations for clinical and business use. Build scalable RAG solutions, vector DB integrations, and production-grade Python services. Implement AI orchestration frameworks, ensure MLOps/LLMOps, monitor observability, and collaborate with product, clinical, security, and engineering teams to deliver secure, compliant healthcare AI capabilities.

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.

Design, build, and deploy production-grade AI/ML models, agentic workflows, and data pipelines using Python, Databricks, LangChain/LangGraph, and Azure. Collaborate with cross-functional teams to translate requirements into scalable LLM applications, implement feature engineering and validation frameworks, and embed responsible AI practices including fairness, security, and monitoring.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Design, build, test, and deliver scalable machine-learning and NLP systems and large-scale data pipelines. Integrate ML models into production, write maintainable tested code, collaborate cross-functionally, and drive research-to-product development in an agile environment.

Lead systems engineering for neuromodulation products from feasibility through market release. Define system architecture and requirements, perform functional analysis and verification/validation, manage risk, design data and integration architectures, support regulatory submissions, coordinate cross-functional teams, and mentor engineers. Travel ~10%.

Lead architecture and productionization of a multi-agent, LLM-based AI platform for identity security. Design agent orchestration, context/memory management, and scalable, low-latency infrastructure. Transition prototypes to hardened enterprise pipelines, set reliability and evaluation frameworks for non-deterministic AI, and mentor senior engineers while partnering with product and security leadership.

Lead and scale an enterprise AI/ML engineering organization to design, build, and deploy reusable AI platforms, agent-ready data strategies, and production-grade AI solutions across corporate functions while ensuring responsible AI, governance, and measurable business outcomes.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

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.

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, 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.

Lead technical direction and deliver large-scale ML platform infrastructure including feature store, model serving, experimentation, and training pipelines. Drive architecture, mentor engineers, coordinate cross-team initiatives, resolve system-wide issues (latency, reliability, GPU utilization), and define safe ML deployment paths from registration through rollout, monitoring, and rollback.

Lead technical presales for Anaplan's AI-infused planning platform: architect AI solutions, run technical discovery, build demos/workshops and proofs-of-concept, collaborate with product and sales teams, and drive complex enterprise AI opportunities to close while representing customer needs to product/engineering.