Top Machine Learning Jobs in Minneapolis, MN

Lead development and operationalization of a clinical reasoning framework for trustworthy clinical AI. Partner with product, engineering, editorial, and design to define decision pathways, evaluation benchmarks, validation protocols, and ongoing expert review. Translate clinical feedback into AI system improvements, represent the team externally, and optionally maintain limited clinical practice.

Lead research and development of signal and image processing algorithms for ultrasound-enabled medical devices. Design, implement, and evaluate real-time algorithms, analyze bench/preclinical/clinical data, and collaborate cross-functionally to translate concepts into validated clinical solutions while providing technical leadership and mentorship.

Develop and evaluate novel AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, document models, collaborate with stakeholders, RUAI, and engineering to productionize solutions and MLOps pipelines, and publish/patent research.

Lead hands-on development and deployment of AI/ML solutions and rapid prototypes (with emphasis on Generative AI/LLMs), embed responsible AI practices, integrate AI into engineering workflows, and scale successful pilots across the enterprise while collaborating with cross-functional leaders.

Lead end-to-end design, implementation, deployment, and operation of production AI/ML-enabled full-stack applications. Build LLM-powered features, design scalable backend services and data models, deploy on Azure, implement CI/CD and observability, and mentor engineers while ensuring secure, compliant integration into clinical workflows.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Design, train, and productionize underwriting ML models for real-time transaction decisions. Build feature pipelines and training datasets, run experiments, integrate models into batch/real-time systems, and monitor data and model health while collaborating with cross-functional teams.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

Drive sales and revenue growth within a named territory by managing the full sales cycle, building executive relationships, meeting revenue targets, collaborating with solutions and delivery teams, negotiating contracts, managing accounts, representing Cardamom at industry events, and supporting marketing and product improvement efforts.

Lead statistical design and analysis for Phase I-IV clinical studies, develop protocols and SAPs, perform efficacy and exploratory analyses, run simulations, apply advanced methods (mixed models, longitudinal), oversee datasets (ADaM/CDISC), use R and SAS for analyses, and provide strategic statistical guidance to cross-functional teams supporting regulatory submissions.

Serve as primary post-sales strategic partner for largest enterprise clients, driving AI Agent adoption, ROI, optimization, governance, renewals, and cross-functional advocacy to turn customers into champions.

Own marketing measurement, operations, and analytics: build and QA campaigns, set goals, measure performance and attribution, manage data infrastructure and segmentation, optimize lead scoring/routing, apply AI/ML for automation and predictive insights, and document scalable processes to drive demand generation and cross-team alignment.

Lead strategy, build and scale a global partnerships ecosystem (ERP, fintech, banks, ISVs, VARs, SIs) to drive partner-sourced pipeline, revenue, and platform expansion. Own partner negotiations, commercial models, cross-functional integration, and team development while leveraging AI/data insights and ensuring compliance.

Lead and grow a team of 5-7 enterprise sales reps in the Bay Area, recruit and develop sales leaders, build business plans, drive new-logo acquisition and account expansion, manage the sales cycle from lead generation to close, develop GTM strategy, and report to the VP of Sales. Role supports ambitious quarterly and annual goals and may require occasional work outside standard hours.

Drive supply chain efficiency through data analytics, KPI development, process redesign, ERP/WMS/automation selection and implementation, Lean Six Sigma continuous improvement, AI-driven forecasting, cross-functional project leadership, audits, and change management with suppliers and 3PLs.

Lead and manage the engineering team to design, build, deploy, and maintain cloud-based social media/influencer marketing and AI platforms. Oversee software architecture, large-scale data pipelines, image/video processing, and AI/ML solutions. Coordinate cross-functional work, hiring, project planning, Agile delivery, and continuous system improvement.

Sell ExtraHops network security analytics to U.S. Federal/Department of War customers by developing territory accounts, engaging senior federal decision-makers, driving new business and renewals, coordinating with resellers and engineers, delivering tailored demos, and executing strategic account plans to meet revenue targets.

Analyze complex datasets, build and deploy predictive models, create visualizations, ensure data quality, and collaborate with business stakeholders to drive data-driven growth.

Provide technical leadership as a Solutions Architect SME for federal health programs. Design and oversee AWS-based solutions, define success metrics, support proposal development, lead multi-disciplinary teams, coordinate SMEs, deliver technical presentations, and develop staffing and lifecycle cost estimates to support deployment and growth across federal agencies.

Design and implement data integration and data warehouse solutions using Big Data and open-source technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and ML concepts, and implement real-time streaming and aggregation patterns. Hands-on SQL/NoSQL and programming experience required.

Lead the vision and execution of data product roadmap; translate business needs into epics and acceptance criteria; partner with data engineering and analytics; run agile ceremonies and manage backlog; champion data-as-a-product; scope and pilot AI/ML capabilities; present roadmaps and business cases to leadership.

The Machine Learning Engineer will build data-intensive products, deploy ML models, analyze datasets, and collaborate with product teams to enhance Nextdoor's platform.

Lead informatics technical strategy and execution for materials engineering programs. Provide architecture and technical authority, drive AI/ML-enabled materials discovery, approve documentation, manage cross-functional risk, mentor engineers, and engage external partners and customers to deliver complex engineering projects.