Top Remote Machine Learning Jobs in Minneapolis, MN

Own and improve ClickUp’s voice AI systems, including real-time speech-to-text, audio processing, context-aware transcription, LLM post-processing, mention resolution, formatting, and voice-to-action workflows. Evaluate and integrate ASR models for accuracy, latency, and cost, while collaborating across product and platform teams to deliver voice and multimodal assistant features across desktop, mobile, web, and browser extension products.

Leads AI governance and assurance engagements, designing policies, controls, operating models, risk assessments, and compliance programs. Evaluates AI security, model governance, third-party solutions, and control effectiveness against frameworks including NIST AI RMF, ISO 42001, and the EU AI Act. Collaborates with security and engineering teams to test threats such as prompt injection, model manipulation, data poisoning, and leakage. Advises executives and clients, develops methodologies and proposals, researches emerging AI risks and regulations, and supports business development.

Leads enterprise CrowdStrike deployments, architecture, compromise assessments, threat hunting, SOC modernization, detection engineering, cyber analytics, Purple Team exercises, and incident response. Advises CISOs and executives, manages cross-functional teams, designs global 24/7 SOCs, and coordinates breach containment and recovery. Requires deep CrowdStrike expertise, cybersecurity leadership, scripting, network and detection engineering, and executive communication skills.

Designs and maintains scalable data models, semantic layers, ETL/ELT workflows, data-quality frameworks, and governed analytics products for commercial operations. Owns data solutions from requirements through deployment, monitoring, documentation, and support. Partners with technical and business stakeholders to deliver trusted reporting, dashboards, scorecards, productivity measurements, and AI-enabled decision support.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Build, productionize, and govern clinical machine learning products using large-scale healthcare data. Responsibilities include developing predictive models, monitoring ML research, supporting MLOps, implementing model governance, integrating analytics into products and dashboards, curating datasets, partnering with clinicians and product teams, mentoring engineers, and communicating findings to technical and executive stakeholders.

Designs, builds, and operates observability and SRE systems that improve IT service visibility, reliability, performance, and resilience. Responsibilities include metrics collection, monitoring, alerting, APM, RUM, synthetic transactions, SLOs, incident response, capacity planning, infrastructure automation, self-healing systems, and production readiness reviews. The role also applies AI-enabled operations, supports on-call rotations, reduces operational toil, and partners with development teams on architecture and chaos testing.

Leads enterprise security reviews, threat modeling, architecture assessments, governance, and control development for AI, generative AI, machine learning, LLM, RAG, and AI-agent solutions. Defines security standards and guardrails, evaluates platforms and vendors, advises cross-functional stakeholders, and supports responsible AI adoption while managing cybersecurity, privacy, regulatory, and operational risks.

Leads Helium 10’s global product management function across the United States and China. Owns multi-quarter roadmaps, new-product discovery and delivery, direct-to-consumer and omnichannel expansion, AI-native product development, customer discovery, commercialization, analytics, and cross-functional execution. Manages and develops product managers, establishes operating standards, ensures timely high-quality releases, and communicates business outcomes to executive stakeholders.

Drive new business revenue across the Western Territory by selling complex healthcare technology and services, including AI-enabled EHR and revenue cycle management solutions. Manage the complete enterprise sales cycle from prospecting, discovery, demonstrations, proposals, negotiations, and closing through accurate forecasting and CRM updates. Build relationships with healthcare decision-makers, manage RFPs, develop territory plans, and travel 50–75% of the time.

Leads Finance of America's AI security and safeguards program, including strategy, governance, red teaming, model and application security testing, threat detection, monitoring, and control validation. Maintains the enterprise AI inventory, evaluates use cases, integrates security requirements into procurement and development processes, and partners with legal, compliance, risk, and security teams. Reports AI risk posture to executives and boards, supports audits and regulatory examinations, and builds a team while remaining hands-on.

Build and lead North’s Applied AI engineering team, taking AI solutions from prototype to production in regulated financial-services workflows. Responsibilities include architecting agentic systems, chatbots, copilots, and ML models; establishing secure enterprise integrations; managing evaluations, monitoring, CI/CD, incident response, and cost controls; and setting technical strategy and responsible-AI governance. The role requires close collaboration with business, product, risk, compliance, legal, and security stakeholders.

Own cloud security across AWS and Azure, including identity, network, application, container, data, and AI security. Build policy and infrastructure controls as code, integrate security into CI/CD, protect regulated health data, and manage detection, incident response, vulnerability remediation, and compliance. The role also develops AI security guardrails and coaches engineering teams while operating production security controls and supporting on-call responsibilities.

Leads enterprise AI solution strategy, architecture, pre-sales, and implementation delivery for customers across the Americas. Defines AI use cases, validates feasibility, designs scalable architectures, guides Professional Services teams, and resolves complex technical challenges. Develops AI service offerings, methodologies, reusable assets, and enablement programs while mentoring architects, consultants, and project leaders. Requires deep experience with enterprise transformations, LLMs, multi-agent systems, generative AI, data architecture, cloud platforms, and production AI deployments.

Supports enterprise AI transformation initiatives by developing and evaluating machine learning, generative AI, data science, retrieval-augmented generation, and workflow automation solutions. Responsibilities include data preparation, feature engineering, model training and validation, AI quality assessment, dashboard and documentation development, and collaboration with technical and business stakeholders on secure, practical AI applications.

Own enterprise technical deployment roadmaps, milestones, acceptance criteria, governance, and post-launch adoption. Coordinate Product, Engineering, Delivery, Support, and Sales teams to resolve blockers, manage resources, map workflows, track KPIs, and demonstrate ROI. Partner with account executives on technical expansion opportunities and establish scalable deployment playbooks, PMO templates, and integration patterns.

Leads AI governance, risk assessment, security, privacy, and compliance programs for generative and agentic AI systems. Coordinates cross-functional teams, reviews customer and regulatory requirements, establishes AI agent management structures, automates governance controls, manages technical improvement projects, supports audits, delivers training, and reports risks and metrics to senior leadership. The role requires extensive cybersecurity experience, AI security and threat modeling expertise, framework compliance, project management, and executive communication.

Own the vision, strategy, and roadmap for Root’s pricing platform and machine learning infrastructure. Lead engineering and partner with data science, actuarial, and engineering teams to improve pricing workflows, model development, deployment, monitoring, and regulatory operations. Drive self-service tooling, LLM-enabled data science workflows, platform reliability, and nationwide expansion while translating business needs into product requirements and prioritized investments.

Performs hands-on cybersecurity and AI engineering, defines and documents functional and technical requirements, converts requirements into Jira stories, validates application features, and collaborates with customers, developers, systems engineers, and AI stakeholders. Evaluates AI architectures, model performance, integration tradeoffs, and emerging AI/ML applications for enterprise software. Requires active TS/SCI with polygraph clearance, U.S. citizenship, a bachelor’s degree, and 15+ years of experience.

Own technical evaluations, deployments, and production integrations for major government and enterprise customers. Build Python, SQL, agents, tools, and REST integrations within customer environments; lead proofs of concept, deployments, executive briefings, and technical account relationships. Triage bespoke versus reusable capabilities, influence the product roadmap, and review other engineers’ work. The role requires strong cloud, containerization, data, communication, and ambiguous problem-solving skills.

Partner with Account Executives on discovery, demos, proof-of-value engagements, RFPs, and enterprise security reviews. Translate on-chain risk into business terms for executives and technical stakeholders, develop customized demonstrations, advise customers on integrations and compliance, and represent the company at industry events. Collaborate with Product, R&D, Security Engineering, and Customer Success to influence product direction, improve sales processes, and support customer onboarding and adoption.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Design and develop scalable backend systems using Kotlin, Micronaut, and Spring. Build RESTful APIs, optimize PostgreSQL and Elasticsearch databases, implement RabbitMQ-based asynchronous processing, integrate AI tools, and create automated tests. Deploy and monitor services on GCP using Docker, Kubernetes, and Terraform. Collaborate cross-functionally, participate in code reviews, troubleshoot performance issues, and deliver reliable backend products.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.