Top Machine Learning Jobs in Minneapolis, MN

Develop novel AI/ML solutions for healthcare using structured and unstructured data. Build agentic, generative AI, deep learning, and traditional ML systems; design experiments and analyze results; communicate findings; support responsible AI compliance; document models; collaborate on MLOps, LLMOps, and Agentic Ops pipelines; and contribute to patents and publications.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Leads enterprise AI architecture strategy, standards, governance, and reference architectures. Designs scalable production AI, generative AI, agentic AI, RAG, LLM, and MLOps solutions while guiding cloud, platform, security, compliance, cost, and integration decisions. Advises technology and business leaders, aligns AI capabilities with business goals, promotes responsible AI practices, tracks emerging technologies, and mentors architecture teams.

Conduct research advancing artificial intelligence and machine learning, implement and evaluate algorithms on healthcare data, communicate findings across interdisciplinary teams, and document results through internal materials, publications, and conference submissions. Potential research areas include foundation models, clinical NLP, generative AI, causal inference, multimodal learning, explainable AI, and privacy-preserving machine learning.

Leads enterprise AI application architecture and hands-on development for LLM, agentic, multi-agent, and RAG solutions. Establishes scalable design patterns, governance standards, observability, reliability, and compliance practices. Collaborates with product, engineering, and compliance teams, evaluates emerging AI technologies, optimizes performance, and mentors senior engineers. The role requires extensive software engineering and enterprise AI architecture experience, with expertise in cloud-native systems, Kubernetes, CI/CD, MLOps, and production AI platforms.

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.

Analyze and automate market risk reporting, reconcile and validate risk data, investigate data quality issues, and build analytics and dashboards. Support VaR, stress testing, P&L attribution, back-testing and model monitoring, and modernize risk processes using VBA, SQL, Python and AI-enabled capabilities while partnering with stakeholders and control functions.

Build and maintain scalable applications and technology supporting Target’s stores, warehouses, checkout, fulfillment, inventory, and supply chain operations. Design and implement end-to-end functionality, influence architecture, optimize system performance and operational health, troubleshoot issues, and develop distributed systems. Collaborate with engineers, data scientists, project teams, partners, and stakeholders while applying modern programming languages, frameworks, event streaming, databases, monitoring, and machine learning technologies.

Develop scalable backend software for Target’s stores and supply-chain technology, including checkout, fulfillment, inventory, demand forecasting, and replenishment systems. Design technical components, solve engineering problems, review code, address operational issues, and contribute to architecture and development ceremonies. Use Java, Kotlin, event streaming, databases, CI/CD, monitoring, and testing technologies in a hybrid environment.

The Lead Engineer architects and develops scalable applications supporting Target’s stores, warehouses, checkout, fulfillment, inventory, forecasting, and replenishment systems. The role serves as the technical anchor for a product, owning application architecture, driving architectural improvements, ensuring system performance and operational health, and approving end-to-end functionality. The engineer partners with teams and stakeholders, applies broad expertise across languages and frameworks, and helps leverage data, analytics, and machine learning to improve retail operations.

Own the vision, strategy, roadmap, and outcomes for Augeo’s Incentives product domain. Partner with AI, data science, engineering, and business teams to build ML-driven incentive decisioning, experimentation, personalization, catalog configuration, and automation capabilities. Translate incentive economics and stakeholder needs into prioritized requirements, measure engagement and ROI, ensure privacy and governance compliance, and lead cross-functional planning and delivery.

Build and deploy finance-focused data, analytics, automation, and AI solutions. Partner with FP&A and other Finance teams to identify high-value problems, develop prototypes, create analytical models and dashboards, and apply GenAI, machine learning, APIs, and workflow automation. Scale successful solutions into secure enterprise capabilities while supporting forecasting, planning, reporting, variance analysis, and decision support.

Analyzes digital product performance across web properties using Adobe Analytics, Google Analytics, tagging systems, statistical modeling, and data visualization. Develops measurement requirements, tracking specifications, dashboards, and actionable insights for product and marketing teams. Troubleshoots tags and data collection, supports experimentation and quality assurance, establishes analytics standards, and mentors analysts while communicating findings to stakeholders.

Build and deploy enterprise agentic AI solutions, including agents, MCP tools, plugins, orchestration architectures, evaluation harnesses, and secure APIs. Integrate agents with search and collaboration platforms, instrument production behavior, manage cost and latency, and test against prompt injection and tool vulnerabilities. The role requires backend software engineering, production agent development, MCP experience, LLM evaluation, and AI coding assistant expertise, with preferred experience in managed runtimes, retrieval systems, and enterprise security.

Lead development and deployment of an agentic AI/ML pipeline for identifying, prioritizing, and pricing high-cost healthcare claims. Responsibilities include model development, confidence scoring, HITL safety design, ML lifecycle management, monitoring, workflow automation, responsible AI, and enterprise-scale analytics using Databricks and Snowflake. The role also evaluates emerging AI technologies and supports regulatory-aware automation and healthcare data transformation.

Designs, deploys, and scales AI and machine learning solutions for enterprise healthcare applications. Responsibilities include building and tuning production models, developing MLOps frameworks and deployment pipelines, monitoring model performance, automating workflows, evaluating emerging AI technologies, and ensuring security, scalability, responsible AI, and governance standards. The role collaborates with software engineers, data scientists, and business stakeholders.

Lead technical client onboarding, migrations, solution design, integrations, data orchestration, troubleshooting, and project delivery for Movable Ink’s AI-powered personalization platform. Partner with clients and internal teams to implement custom workflows, APIs, webhooks, and data solutions. Conduct training and workshops, provide product expertise, advise on roadmap improvements, and support long-term client success across email and mobile marketing programs.

Leads underwriting innovation by identifying opportunities for AI, automation, and emerging technologies to improve workflows, decisions, and user experiences. Partners with underwriting, product, and technology teams to translate business challenges into concepts, evaluate solutions and feasibility, and support implementation. Requires underwriting expertise, analytical and communication skills, stakeholder alignment, and experience applying technology to workflow or process improvements. The role is remote within the U.S. with up to 10% travel.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Lead and grow a data science team building production machine learning models. Oversee the full model lifecycle, including data sourcing, feature engineering, training, deployment, and monitoring. Coach technical staff, manage planning and executive communication, investigate fraud patterns, improve model performance, and deliver dependable production code for real-time decisions. Present analyses that guide data strategy, product direction, operations, marketing, and sales.

Leads customer-facing technical delivery from demonstrations and requirements discovery through architecture, implementation, testing, and production launch. Builds AI-driven agents, analytical products, predictive modeling workflows, and data pipelines; integrates solutions with client CRMs, consumer applications, and varied data sources. Partners with client technical teams, presents to senior stakeholders, and feeds field learnings back to the core platform team. Requires occasional travel and strong Python, TypeScript, architecture, cloud, and communication skills.

Build, train, evaluate, and deploy production machine learning and LLM systems for legal document intelligence. Develop NLP solutions for information extraction, summarization, search, AI agents, and document analysis. Create scalable ML pipelines, evaluation frameworks, and reliability metrics while optimizing model performance. Collaborate with engineering, product, legal, data, and security teams to deliver secure AI capabilities and translate business problems into production solutions.

Lead the architecture, development, integration, and validation of AI-enabled autonomy for UAS, drone swarms, robotic platforms, and C2 systems. Develop real-time decision-making, sensor fusion, navigation, multi-agent coordination, and edge AI capabilities. Build simulation and digital-twin environments, conduct hardware-in-the-loop and flight testing, integrate tactical systems, and improve resilience against contested environments. Lead engineering teams, manage AI/ML governance and deployment, support DevSecOps and cybersecurity compliance, and advise mission stakeholders.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.