Top Senior Machine Learning Jobs in Minneapolis, MN

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Own the clinical infrastructure and program portfolio powering Arine’s medication management platform. Evolve the clinical recommendation engine, expand data inputs, standardize program configuration, and create repeatable frameworks for launching and scaling clinical programs. Partner with clinical, engineering, data science, implementation, and customer success teams to translate needs into requirements, improve onboarding, maintain regulatory alignment, and prioritize work using clinical safety, user value, and business impact.

Leads engineering for one or more product-domain teams, partnering with Engineering Managers, Product, Data Science, Analytics, and Insurance stakeholders. Responsibilities include setting technical strategy and roadmaps, coaching engineers, driving cross-team initiatives, contributing code, leading incident response and resiliency improvements, and establishing engineering standards. Requires strong Ruby on Rails expertise, Rust familiarity, software leadership experience, and excellent communication skills.

Lead the demand generation function by translating go-to-market priorities into segmented campaigns across email, search, social, content, webinars, and events. Own HubSpot automation, Salesforce integration, lead scoring, funnel analytics, marketing-sourced pipeline, budgets, and agency performance. Collaborate with Sales and Commercial Operations to improve lead handoffs and conversion. Apply AI and automation to increase execution efficiency while coaching team members and building the future demand generation team.

Leads AI/ML engineering teams and technical strategy for enterprise-scale healthcare and pharmacy solutions. Oversees development, deployment, governance, MLOps, security, and operations for generative AI, LLM, NLP, predictive analytics, and agentic AI capabilities. Partners with product, engineering, data science, and executives to align technology with business goals, evaluate emerging technologies, improve automation, and develop engineering talent.

Leads new business development, account growth, commercial discussions, client relationships, and sales operations for AI and GenAI data solutions. Partners with engineering, delivery, and customer success teams to understand frontier lab needs, structure projects, close opportunities, and expand client accounts. Requires quota-based technology sales experience, startup experience, STEM education, and strong AI/ML market knowledge.

Develop and implement data science, predictive modeling, machine learning, simulation, and data engineering solutions for laboratory automation and digital R&D products. Lead pilots from opportunity identification through adoption, collaborate with scientists and technology teams, improve laboratory data quality and usability, integrate models into workflows, and measure business impact. The role requires technical leadership across multidisciplinary teams and contributes to the long-term Digital Lab roadmap.

Develops and implements digital science capabilities across laboratories, including predictive modeling, simulation, machine learning, AI-enabled workflows, data-driven experimentation, and lab automation. Partners with scientists, product developers, modeling teams, and data specialists to convert R&D challenges into scalable solutions. Leads digital science pilots, improves laboratory data quality and adoption of digital tools, evaluates emerging technologies, measures business impact, and contributes to the Digital Lab roadmap.

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.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Lead the vision, strategy, and evolution of LTK’s creator experience and commerce products. Define creator workflows, identify AI-powered opportunities, analyze behavioral data, establish outcome metrics, and drive experimentation to improve engagement, retention, earnings, and product quality. Collaborate across Product, Engineering, Design, Data, and Creator Success while influencing senior stakeholders and guiding complex, multi-quarter initiatives.

Build product enablement for global go-to-market teams across AI Agents, APIs, and Creative products. Responsibilities include preparing launch workshops, competitive training, product certifications, onboarding and everboarding content, hands-on demos, role-play simulations, practice environments, and performance measurement. The role partners with product marketing, engineering, research, and enablement teams while using ElevenLabs’ AI technology to create training experiences.

Owns CSL Plasma’s target-state business architecture, capability models, value streams, architecture decisions, integration standards, and technology roadmaps. Translates business strategy and regulatory requirements into architecture blueprints across digital products, while governing AI, automation, data, and integration patterns. Partners with product, engineering, enterprise architecture, security, privacy, and compliance teams to drive committed designs, architectural consistency, and migration sequencing across a complex regulated environment.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Define the vision, strategy, and roadmap for AI-powered products. Lead use-case evaluation, requirements, experimentation, development, deployment, adoption, and continuous improvement across Generative AI, LLM, RAG, and agentic solutions. Collaborate with engineering, data science, design, and business stakeholders; establish success metrics; manage AI risks; and communicate product value, progress, and strategy to leadership and clients.

Lead enterprise data and AI architecture across marketing, customer engagement, digital, sales, and analytics. Design scalable cloud data platforms, reusable data products, semantic models, real-time and batch architectures, governed AI capabilities, machine learning systems, generative AI, agents, recommendations, and personalization. Establish standards for governance, security, privacy, quality, lineage, and observability. Provide technical direction, lead architecture reviews and roadmaps, partner with engineering and data science teams, and mentor architects and engineers.

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 enterprise security operations, resilience, incident response, threat intelligence, cloud security, IAM, and risk management. Serve as deputy to the CISO and primary incident commander while advising executives on cyber risk. Build and scale multidisciplinary security teams, modernize cloud-native defenses, embed security-by-design across products and AI/ML systems, and establish metrics-driven governance, automation, regulatory alignment, and business-focused security strategies.

Senior Product Designer responsible for creating high-craft, 0-to-1 AI recruiting and talent product experiences. The role involves visual and interaction design, rapid iteration, prototyping, design-system development, production-ready delivery, and experimentation with generative AI. Candidates should have strong Figma expertise, exceptional visual design skills, experience shipping SaaS, enterprise, or AI products, and a portfolio demonstrating creative range and execution.

Leads data engineering teams while designing and optimizing scalable batch and real-time data pipelines on cloud platforms. Responsibilities include developing PySpark and Spark workflows, orchestrating Airflow DAGs, managing cloud infrastructure, implementing CI/CD, improving data quality and performance, and mentoring engineers. The role collaborates with clients, product, architecture, and program management teams, while applying generative AI and DevOps practices to improve engineering productivity and operational efficiency.

Design the full Search experience across organic and sponsored results, including typeahead, ranking, filters, empty states, and monetization. Establish design principles and components, partner with product, research, data science, and ML engineering, and use experimentation to improve engagement, relevance, and revenue. The role requires substantial search or discovery design experience, strong product craft, consumer instincts, and comfort designing alongside AI and monetization teams.

Leads Propel America's product and technology strategy, including the Fellow Portal, AI integrations, technology roadmap, software development, Salesforce, infrastructure, data systems, cybersecurity, and vendor relationships. Manages technical staff and outsourced IT providers, oversees architecture, QA, releases, compliance, data privacy, and disaster recovery, and partners cross-functionally to translate organizational needs into scalable products and technology solutions.

Leads enterprise-wide technical and transformation initiatives across Product, Technology, Operations, Data, Security, and Innovation. Responsibilities include program governance, requirements gathering, product documentation, architecture and process modeling, delivery coordination, risk and dependency management, AI use-case evaluation, vendor assessment, and executive stakeholder communication. The role establishes documentation standards and supports modernization through Agile practices, integrations, automation, and responsible AI governance.