Top Machine Learning Jobs in Minneapolis, MN

Designs and deploys proactive cyber defense and counter-adversary capabilities. Responsibilities include threat research, adversary behavior analysis, threat hunting, security automation, detection logic, attack-surface evaluation, vulnerability mitigation, and telemetry improvements across network, cloud, and endpoint environments. The role collaborates with threat intelligence and incident response teams and mentors security personnel on defensive engineering and advanced threat mitigation.

Conduct advanced AI, generative AI, agentic AI, and machine learning research from ideation through implementation, evaluation, and documentation. Build and evaluate foundation models and agentic systems using heterogeneous data, collaborate with interdisciplinary teams, and translate research into production financial applications. The role includes publishing in leading conferences and journals, contributing to patents and open-source work, applying modern AI frameworks and GPU infrastructure, and transferring models into production.

Leads AI innovation and adoption across a healthcare advisory practice. Develops and deploys predictive models, generative AI, machine learning, and analytics solutions for value-based care and payment challenges. Oversees data readiness, governance, responsible AI reviews, model monitoring, stakeholder adoption, and client delivery. Translates complex analytics into executive-ready insights, dashboards, and presentations while building reusable assets, training teams, and driving enterprise AI strategy.

Leads healthcare analytics service delivery across Medicare, Medicaid, and Commercial programs. Designs analytic models, dashboards, reports, and predictive solutions; translates complex data into client-ready insights; guides data governance and management practices; and promotes AI, machine learning, generative AI, and automation to improve reporting efficiency. Partners with clients, analysts, developers, subject matter experts, and AI teams to analyze claims, enrollment, provider, and member data and support value-based payment initiatives.

Leads enterprise technology, data, analytics, automation, and AI transformation initiatives from strategy through delivery. Defines roadmaps, budgets, success metrics, and resource plans; manages cross-functional execution, dependencies, vendors, governance, and executive reporting. Partners with product, engineering, architecture, data science, and business teams to deliver scalable solutions. Drives responsible AI adoption, organizational change, operational readiness, and continuous improvement while mentoring cross-functional teams.

Leads enterprise data and AI engineering strategy across business units. Responsibilities include modernizing cloud data platforms, establishing governance and semantic layers, enabling self-service and AI-powered analytics, building reusable data products and models, and supporting machine learning, generative AI, RAG, and agentic AI architectures. The role manages data engineering, architecture, AI engineering, and analytics enablement teams while partnering with executives and business stakeholders to deliver trusted, scalable data capabilities.

Monitors AI solution registrations, security reviews, compliance evidence, approved use records, telemetry, and control performance. The role triages alerts, policy violations, exceptions, incidents, and control gaps; supports root cause analysis and remediation; maintains audit-ready workpapers, logs, repositories, and metrics; and coordinates with engineering, application, identity, data protection, vendor risk, and Responsible AI teams. It also provides operational support for AI security governance processes.

Leads record-to-report finance transformation within AI pods, blueprinting accounting processes and designing future-state workflows. Partners with Finance, Data, Technology, Risk, and Audit teams to develop and validate AI proof-of-concepts for close, journals, reconciliations, consolidation, reporting, and statutory processes. Ensures accounting accuracy, SOX/ICFR compliance, auditability, human approval controls, data lineage, and responsible AI governance while supporting enterprise adoption and scaling.

Own the end-to-end strategy, roadmap, delivery, and optimization of enterprise AI products. Lead the productionization of generative, agentic, and applied AI solutions, including LLM applications, RAG pipelines, and autonomous workflows. Partner with engineering, data science, design, and business teams on architecture, data readiness, Agile execution, model lifecycle management, governance, security, and responsible AI. Define success metrics, manage stakeholders, and ensure measurable operational and business impact.

Develop and deploy AI and machine learning solutions, analyze complex enterprise datasets, build predictive and optimization models, conduct statistical experiments, and translate findings into business recommendations. The role includes applying deep learning, NLP, generative AI, and ethical AI practices; creating scalable decision-support frameworks; evaluating emerging technologies; collaborating across teams; and mentoring junior data scientists and analysts.

Design and deliver scalable enterprise architecture solutions across cloud environments. Lead architectural planning, build POCs, integrate AI-powered features responsibly, mentor engineering teams, and ensure designs meet security, reliability, and maintainability standards.

Leads enterprise software engineering strategy, AI and machine learning adoption, cloud platform architecture, legacy modernization, and generative AI deployments. Oversees engineering managers and principal engineers, establishes governance and operational standards, mentors technical teams, and aligns technical roadmaps with executive business goals. The role also drives ethical AI practices, MLOps governance, scalable software delivery, organizational hiring strategies, and cross-functional technology transformation across healthcare systems.

Lead enterprise AI/ML engineering for Medicare & Retirement, defining AI strategy, building scalable MLOps platforms, embedding GenAI and ML into products and operations, ensuring responsible AI and compliance, and delivering measurable business value through production-grade AI solutions and platform transformation.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Owns the strategy, backlog, requirements, and delivery of AI-powered conversational assistants and proactive outbound engagement solutions. Collaborates with engineering, data science, architecture, and business teams to define technical specifications, model criteria, testing strategies, and product metrics. Evaluates emerging AI and contact center technologies, manages Agile workflows, and drives continuous product improvement across digital consumer experiences.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Supports commercial enterprise sales by discovering customer requirements, aligning SailPoint solutions, delivering demonstrations, responding to RFPs, and leading Proof of Value evaluations. The role partners with sales, product, customer success, professional services, and channel teams while communicating technical and business value to IT and business stakeholders. It requires substantial pre-sales experience, enterprise software knowledge, SaaS architecture familiarity, and expertise in identity governance and access management.

Lead Runpod’s Americas sales organization and revenue number across new business, expansion, and renewals. Manage and coach Account Executives and Account Managers, oversee forecasting and pipeline, lead complex enterprise deals and contract negotiations, build regional go-to-market strategy and sales playbooks, and convert product-led demand into revenue. Partner with Marketing, Product, Legal, and Finance while improving sales efficiency through AI and developing a remote-first team.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Own the strategy, roadmap, packaging, and go-to-market evolution of Pluralsight’s AI, Cloud, and Security learning solutions. Design blended learning programs, define expert perspectives, identify content and lab components, monitor market needs, and use customer and learner data to improve outcomes. Partner with Product, Sales, Marketing, Curriculum, Engineering, and Services teams, support strategic enterprise deals, manage solution benchmarks, and drive adoption, renewals, and revenue growth.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Leads customer-facing solution implementation and pre-sales activities for SailPoint’s largest accounts. Responsibilities include discovering requirements, mapping them to SailPoint solutions, participating in foundational implementations and proof-of-value engagements, explaining architecture and best practices, collaborating with sales and cross-functional teams, and developing AI- and agentic technology demonstrations. The role becomes a technical lead and trusted advisor while guiding complex customer engagements and mentoring less experienced team members.

Lead and scale a global team of Principal Agentic Technologists supporting strategic enterprise sales. Define technical sales methodologies, demonstrations, and proof-of-value frameworks for AI agent and non-human identity security products. Partner with sales, product, marketing, and engineering leaders to support major deals, influence product roadmaps, develop enablement programs, and communicate technical value to executives and architects.