Top Remote Machine Learning Jobs in Minneapolis, MN

Supports production data science capabilities by monitoring models, automations, pipelines, dashboards, and decision-support tools. Troubleshoots issues, evaluates performance, maintains documentation, and recommends improvements. Develops scalable data pipelines, workflows, analytical processes, dashboards, and AI-enabled solutions while conducting ad hoc analyses and supporting validation and deployment. Partners with senior data scientists and business stakeholders to operationalize research, communicate findings, and expand ownership across analytical portfolios.

Develop and optimize advanced analytics, machine learning models, prioritization logic, scoring methodologies, and production analytical solutions for healthcare provider engagement and quality outcomes. Own the full lifecycle from problem definition through monitoring, collaborate with data and business stakeholders, translate findings into recommendations, and improve the Key Measures Model using new data, methodologies, and AI/ML technologies.

Leads underwriting innovation by identifying opportunities for AI, automation, and emerging technologies to improve workflows, decisions, and user experiences. Partners with underwriting, product, and technology teams to translate business challenges into concepts, evaluate solutions and feasibility, and support implementation. Requires underwriting expertise, analytical and communication skills, stakeholder alignment, and experience applying technology to workflow or process improvements. The role is remote within the U.S. with up to 10% travel.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads high-visibility product programs focused on market adoption, analyst relations, customer advisory boards, executive briefings, and industry events. Translates technical product roadmaps into compelling market value propositions, synthesizes analyst and customer feedback, and provides actionable insights to product leadership. Partners cross-functionally with Product, Marketing, and executive stakeholders while supporting strategic initiatives and external-facing communications.

Leads technical customer engagements for strategic enterprise accounts, combining solution engineering, software implementation, consulting, and sales support. Responsibilities include discovering requirements, designing and documenting SailPoint solutions, conducting foundational implementations and proof-of-value engagements, educating customers on architecture and best practices, and developing AI-enabled demonstrations and agentic solutions. The role collaborates across sales, product, professional services, partners, and customer success, and may require up to 15% travel.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

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.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

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 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.

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.

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.

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.

Defines and governs enterprise cybersecurity architecture across cloud, infrastructure, applications, data, AI, identity, and cyber defense. Establishes security principles, reference architectures, standards, roadmaps, and Zero Trust patterns; influences technology strategy, architecture decisions, modernization, integrations, and build-versus-buy choices. Partners with engineering, enterprise architecture, risk, legal, and business teams to implement scalable security capabilities, improve resilience, reduce architectural complexity, and address emerging threats across complex enterprise environments.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Own the strategy and roadmap for patient-provider search, retrieval, ranking, and marketplace matching. Lead experimentation, objective functions, evaluation, and ML platform foundations while partnering with ML engineers, data scientists, design, clinical teams, and executives. Improve successful, lasting care matches rather than optimizing only clicks or bookings. Balance patient access, provider growth, clinical quality, and payer priorities, while setting the standard for technically grounded product management.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.