Top Senior Machine Learning Jobs in Minneapolis, MN

Lead deployment, configuration, optimization, and operational support of CrowdStrike Falcon AI/ML detection and response capabilities. Tune ML models, build automation playbooks, develop scripts using Falcon APIs, integrate AI features with customer toolchains, support incident response and threat hunting, deliver customer workshops, and mentor team members. Travel up to 25%.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Lead scientific and strategic AI efforts for Vaccines Medical: define AI vision and roadmap, govern responsible AI standards, prioritize and deliver validated AI-enabled solutions, partner cross-functionally, oversee deployment and adoption, manage budget and vendors, and ensure regulatory, ethical, and scientific compliance across global medical affairs.

Lead and architect high-value healthcare analytics engagements, build reusable analytic frameworks (DBT), partner with product and engineering, mentor analysts, ensure analytic quality, and represent Arcadia externally to drive population health and value-based care outcomes.

Own Secret Detection and Vulnerability Research as a product: define strategy across prevention, detection, validation, revocation, and reporting; ship detection content and intelligence with measurable quality; drive adoption, retention, and revenue; partner with engineering, research, and Field; use AI to accelerate research and productization.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Lead AI/ML strategy for Medicare & Retirement, identify and prioritize high-impact AI use cases, secure funding, and oversee end-to-end delivery of production-grade AI/ML and Agentic AI solutions. Build and mentor large AI engineering teams, embed responsible and secure AI practices, partner cross-functionally, and communicate outcomes to senior leadership.

Lead design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead and transform the Fraud Intelligence Team by designing scalable operating models, QA and measurement frameworks, and talent programs. Drive labeling quality, operational metrics, and fraud prevention impact; convert frontline fraud observations into insights for Product, R&D, Analytics, and GTM; leverage AI, automation, and tooling; and serve as a cross-functional fraud subject-matter expert while developing early-career talent and measurable business outcomes.

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.

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 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.

Lead product vision and go-to-market for an early-stage fraud and abuse detection product in payment integrity. Own strategy, roadmap, and commercialization from pilot to scale; collaborate with engineering, data science, and GTM to deliver AI-driven detection capabilities; define KPIs, measure ROI, and iterate based on analytics and client feedback; monitor market and regulatory trends to maintain differentiation and drive adoption across payer clients.

Lead design and implementation of Headway's insurance systems (eligibility, pricing, matching, claims). Build data-intensive, ML-powered platforms and automation, mentor engineers, write high-impact code, and improve accuracy and reliability across state and payer complexity to scale insurance-covered therapy.

Leads an AI/ML engineering team building and operating enterprise intelligence capabilities, including LLM workflows, RAG pipelines, agentic systems, copilots, classifiers, and evaluation frameworks. Establishes architecture, safety guardrails, testing, observability, governance, and production-readiness standards. Partners with data engineering, security, privacy, compliance, and architecture teams while mentoring engineering talent and driving strategic innovation.

Design and operate scalable batch and streaming data platforms supporting machine learning and generative AI. Build pipelines for structured, unstructured, OCR, document, and image data; develop RAG, semantic search, and LLM-powered solutions; and establish data quality, observability, governance, orchestration, and deployment practices. Partner with stakeholders, lead platform scalability and cost optimization, mentor engineers, and translate ambiguous needs into production-ready technical roadmaps while securely handling sensitive data.

Lead design, development, and enterprise deployment of scalable production ML/AI solutions for pharmacy services. Own MLOps pipelines, model training, validation, containerized deployment, monitoring, and architecture. Collaborate with data scientists, engineers, and business leaders, evaluate emerging AI/LLM technologies, and provide technical leadership and mentorship.

Manage an AI transformation program within medical device R&D by coordinating cross-functional workstreams, schedules, milestones, budgets, risks, dependencies, governance, and deliverables. Facilitate project meetings, track actions and decisions, communicate status to leadership, and drive accountability across technical, quality, regulatory, IT, and external partner teams. The role supports AI architecture, solution delivery, data engineering, and change enablement in a regulated environment.

Lead email channel performance by defining KPIs, testing, analytics, and optimization across campaigns and lifecycle journeys. Drive deliverability, list health, segmentation, personalization, and data-driven experimentation. Partner with IT, analytics, CRM, and marketing teams to implement improvements, ensure tracking and compliance, and mentor email marketers.

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.

Manage and mentor a global team of data engineers and data scientists, align technical delivery with business goals, allocate resources, improve delivery processes, drive automation and technical evangelism, and collaborate with sales and account teams to support growth.