Top Remote Machine Learning Jobs in Reno, NV

Leads the strategy, architecture, implementation, governance, and commercialization of the enterprise data platform. Builds cloud data infrastructure, pipelines, models, DataOps practices, audience intelligence capabilities, and privacy-safe data products. Partners with executive and cross-functional teams to support analytics, personalization, AI, machine learning, reporting, and revenue opportunities. This hands-on technical leader also manages strategic vendors, drives enterprise data initiatives, and communicates technical strategy to non-technical stakeholders.

Leads the university’s information security architecture and engineering strategy, including cloud, network, endpoint, application, data, identity, and AI security. Establishes zero trust, DevSecOps, infrastructure-as-code, and security-as-code practices; oversees architecture, technology selection, compliance alignment, automation, and AI security initiatives. Builds roadmaps, secures funding, manages strategic initiatives, and leads, recruits, mentors, and develops security architects, engineers, and managers while advising executive leadership.

Provides technical leadership for architecture and evolution of large-scale distributed data platforms supporting Azure network telemetry. Designs streaming and batch systems, establishes reusable engineering patterns, drives cross-team technical strategy, and owns capabilities from design through production operations. Ensures scalability, reliability, security, performance, data quality, and operational excellence while mentoring engineers and influencing technical leaders. The role also involves architectural reviews, dependency management, risk reduction, and applying AI or machine learning approaches to operational systems.

Lead strategic sales of AI Workforce solutions, including Microsoft Copilot, AI agents, Viva, Windows 365, and Azure Virtual Desktop. Develop customer strategies, identify opportunities, build pipeline, conduct consultative business and technical conversations, and orchestrate internal teams and partners to close complex deals. The role requires expertise in technology sales, AI, forecasting, demand generation, stakeholder engagement, competitive positioning, and cross-selling across enterprise and regulated customers.

Leads architecture and production delivery of a near-real-time observability platform for large-scale AI supercomputers. Responsibilities include telemetry ingestion, processing, storage, querying, topology and health modeling, 1P/3P data convergence, AI-assisted anomaly detection, technician dashboards, repair validation, security, reliability, cross-team technical leadership, and mentoring.

Conduct life insurance underwriting audits and complex case reviews, assess mortality and risk data, make decisions within an $8 million authority, and evaluate automated underwriting models. Partner with engineering, data science, actuarial, compliance, and customer experience teams to identify rule gaps, improve audit processes, and maintain underwriting accuracy as automated systems evolve.

Leads advanced analytics, AI, automation, and scientific computing initiatives supporting pharmaceutical clinical development. Translates scientific and business requirements into scalable analytical products, software specifications, decision-support tools, and production automation. Partners across biometrics, clinical development, IT, and business teams; evaluates emerging AI technologies; improves regulated workflows; establishes value metrics; drives adoption and change management; and mentors technical talent.

Conduct research at the intersection of AI, cybersecurity, and computer systems. Formulate research questions, design experiments, build prototypes, evaluate AI and security methods, analyze real-world security data, and communicate findings through reports, presentations, and potential publications. The role combines research with practical engineering and offers mentorship while developing independence.

Leads Payment Integrity strategy and audit optimization by identifying claims-payment anomalies, reimbursement trends, provider behaviors, and new data-mining opportunities. Develops and tests audit concepts, evaluates financial and operational impact, and guides successful concepts through implementation across prepay and postpay processes. Optimizes existing audits, influences cross-functional stakeholders without direct authority, provides executive recommendations, and supports sustainable medical cost savings.

Leads Humana’s enterprise digital platform portfolio, defining product vision, investment priorities, adoption strategies, governance, and value-realization metrics. Oversees platforms including Adobe Experience Platform, digital health services, consent management, content capabilities, and the enterprise design system. Partners with business, design, technology, and engineering leaders to align roadmaps, drive reuse and accessibility, manage dependencies, and improve customer and business outcomes. Develops senior product leaders and cross-functional teams while guiding executive investment decisions in a regulated environment.

Build and evolve machine learning tooling and infrastructure for AI-powered clinical solutions, including automated prompt tuning, feedback loops, model benchmarking, experimentation, and evaluation. Partner with data science, clinical, software engineering, and product teams to develop scalable solutions, solve ambiguous technical problems, establish engineering standards, and provide technical mentorship without direct management responsibility.

Leads the enterprise Data Science and AI vision, strategy, roadmap, and global organization. Oversees production-grade machine learning, Generative AI, Agentic AI, predictive modeling, AI governance, deployment, monitoring, cost optimization, and responsible AI. Partners with executive, product, engineering, commercial, legal, operations, and customer success stakeholders to deliver scalable AI products, automation, improved customer outcomes, and measurable business value.

Designs and scales AI-driven security GRC automation, including compliance evidence collection, control testing, risk assessment, audit preparation, and workflow integration. Builds scripts, bots, APIs, and AI/LLM solutions; integrates GRC platforms with security and IT systems; establishes quality controls and guardrails; monitors automation risk and performance; and partners with technical and business stakeholders to improve security compliance maturity.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Own and scale Jellyfish’s security engineering program, including strategy, architecture, secure SDLC governance, AI security, threat modeling, vulnerability remediation, incident response, penetration testing, and bug bounty programs. The role combines hands-on software development with executive advising, cross-functional leadership, mentorship, and security risk management across SaaS infrastructure and customer-facing AI products.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead product strategy for AI-driven business automation and a monetized data-as-a-service offering. Build enterprise AI governance, agent operating models, evaluation standards, privacy controls, and responsible AI practices. Develop an AI champion network, prioritize automation opportunities, and measure productivity gains. Define data products, customer segments, pricing, packaging, and go-to-market plans while partnering across engineering, security, legal, privacy, risk, and business units.

Own post-sale relationships with referral partners, driving adoption, retention, renewals, expansion, and revenue growth. Serve as a strategic advisor, lead business reviews, resolve escalations, coordinate cross-functional teams, enable product usage, and advocate for partner needs. Maintain partner records and report on satisfaction, renewal, and revenue metrics while developing success strategies and playbooks.

Own the strategy, roadmap, requirements, launch, adoption, and success metrics for a technical platform. Partner with engineering, data, product, and business teams; evaluate architecture, APIs, and system tradeoffs; prioritize foundational investments against customer and revenue outcomes; and apply AI to improve platform capabilities. The role requires strong product management experience, technical depth, analytical skills, and production experience with LLM-based or ML-adjacent products.

Leads talent acquisition strategy for specialized AI, machine learning, robotics, automation, and advanced research roles. Sources and closes highly technical candidates, advises senior leaders on talent markets and compensation, mentors recruiters, builds research and industry relationships, and improves recruiting processes using analytics, recruiting technology, and Lean methods.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages sprint planning, releases, DevOps metrics, project delivery, and stakeholder communication. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, and ensures reliable, well-documented data solutions that support organizational reporting and analytics needs.

Manages a business analytics team that delivers insights, KPI reporting, trend analysis, data visualizations, governance, and actionable recommendations. Guides statistical analysis, data modeling, querying, experimentation, and self-service analytics while aligning practices with enterprise standards. Partners with leaders to identify opportunities and risks, improve data quality, drive analytics adoption, and support strategic and operational decisions. Oversees team performance, department projects, communications, and best-practice development.

Lead the design and development of Azure Compute Fleet capabilities that orchestrate Virtual Machine Scale Sets across regions. Build distributed control-plane services, APIs, and workflows for capacity placement, scaling, failover, and lifecycle management. Collaborate across Azure engineering teams, drive AI-enabled engineering improvements, participate in on-call operations, and improve reliability, observability, performance, and recovery for hyperscale cloud services.

Leads global people analytics and workforce intelligence strategy, building AI-driven tools, predictive models, dashboards, and decision-support applications. Partners with IT, data, and security teams on governance, privacy, and ethical AI. Advises executives, democratizes workforce insights, measures people-program ROI, and anticipates talent trends through benchmarking and workforce modeling.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.