Top Senior Machine Learning Jobs in Minneapolis, MN

Leads multiple software engineering teams modernizing large-scale medical transaction batch systems. Drives AI adoption and AI-driven development practices for migration, refactoring, testing, and architecture. Oversees cloud platforms, microservices, CI/CD, security, performance, and operational reliability while partnering with product, architecture, and executive stakeholders. Develops engineering talent, establishes delivery roadmaps, and promotes responsible AI, data governance, and healthcare security compliance.

Leads the design, development, and deployment of Agentic AI, Generative AI, NLP, and machine learning models for consumer-facing applications. Translates business needs into scalable technical solutions, guides data scientists, collaborates with business and technology partners, communicates model outcomes, and ensures regulatory and risk-management compliance.

Leads client-facing AI/ML and emerging technology initiatives from discovery through production. Translates ambiguous healthcare business problems into scalable solutions using GenAI, LLMs, RAG, Agentic AI, and modern data platforms. Directs multidisciplinary teams, manages delivery scope and stakeholder expectations, embeds responsible AI and governance, and develops reusable accelerators for healthcare payer and provider clients.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Leads payer-focused AI consulting engagements from opportunity discovery through implementation. Builds executive relationships with health plans, identifies AI opportunities, leads proposals and negotiations, shapes scopes and commercial models, and oversees cross-functional delivery. Accountable for client satisfaction, delivery quality, revenue, margin, utilization, pipeline, and measurable business outcomes. Develops repeatable consulting offerings, promotes responsible AI practices, and coaches multidisciplinary teams.

Leads the provider-focused AI solutions portfolio, including roadmap development, commercialization, go-to-market enablement, pricing, partnerships, and performance management. Translates AI research and capabilities into market-ready products that deliver measurable value for healthcare providers. Partners with engineering, operations, finance, legal, compliance, sales, and delivery teams while managing executive stakeholders and cross-functional teams. The role requires extensive experience in AI strategy, healthcare provider domains, consulting, commercialization, partnerships, and enterprise transformation.

Leads the AI solutions portfolio for HR and workforce offerings, including roadmap development, commercialization, go-to-market enablement, pricing, partnerships, client engagement, and performance management. Drives enterprise AI adoption and responsible AI practices while translating research and emerging capabilities into market-ready products. Partners with engineering, sales, operations, finance, legal, compliance, and executive stakeholders to deliver measurable customer and business outcomes.

Own the technical product strategy and roadmap for an enterprise healthcare claims data layer spanning more than 2,000 tables. Lead AI-ready data product development, data modeling, governance, security, quality, pipelines, and conversational analytics capabilities. Partner with engineering, cloud, and AI teams to translate business requirements into specifications and backlogs while monitoring performance and continuously improving data products.

Leads the research, development, and deployment of AI, computer vision, and autonomy technologies for aerospace platforms. Develops navigation, obstacle detection, and other autonomous vehicle capabilities; creates research and technology strategies; transitions solutions into UAV and UAM products; pursues government and internal funding; leads complex R&D programs; and mentors AI engineers.

Design, deploy, and scale responsible AI and machine learning solutions supporting healthcare provider experiences. Lead AI initiatives, provide technical direction to AI engineers, evaluate emerging technologies, and partner with product, data, and business teams. Ensure models meet enterprise standards for performance, security, governance, and scalability while contributing to cloud modernization. The role emphasizes generative AI, LLMs, conversational AI, API development, cloud hosting, and production operations.

Lead end-to-end generative AI and machine learning initiatives, designing scalable and secure AI architectures and developing predictive, prescriptive, optimization, NLP, deep learning, and multimodal models. Analyze healthcare datasets, conduct experiments and A/B tests, evaluate emerging approaches, optimize production systems, and translate findings into executive-level recommendations. The role also emphasizes responsible AI, including fairness, transparency, and accountability.

Develop and optimize full-funnel, multi-channel demand generation campaigns that generate pipeline, accelerate deals, and support cross-sell and upsell opportunities. Partner with product marketing, sales development, content, product, field, partner marketing, and analytics teams. Build lead-nurturing programs, align assets to buyer stages, monitor conversion and ROI metrics, and create dashboards to optimize campaign performance.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Develops compliance analytics for AML, sanctions, and consumer compliance programs. Builds data pipelines, dashboards, scorecards, automated reporting, and data quality processes using dbt, GitLab, Python, SQL, Tableau, and Snowflake. Translates regulatory requirements into actionable insights, risk indicators, and metrics while communicating findings to senior management and regulatory stakeholders.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Leads enterprise AI transformation across Medicare shopping, sales, agent, partner, and support experiences. Owns the unified product portfolio strategy, roadmap, AI voice and conversational IVR experiences, cross-channel journey continuity, shared knowledge and orchestration capabilities, responsible AI governance, measurement, and operational readiness. Partners with engineering, architecture, data, design, compliance, and operations teams to deliver scalable AI-enabled products, improve customer and employee experiences, and develop a multidisciplinary product organization.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

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.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.