Top Remote Machine Learning Jobs in Greensboro, NC

Lead the design, development, deployment, and governance of advanced analytics, machine learning, and AI solutions using large healthcare datasets. Responsibilities include data preparation, statistical and predictive modeling, model monitoring, explainability, visualization, stakeholder communication, and translating findings into clinical and operational improvements. The role also mentors data professionals, advises leaders, manages initiative priorities and risks, and promotes responsible AI and Agile practices.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Lead and transform the Fraud Intelligence Team into a high-impact function by improving operating models, QA, tooling, and metrics; build entry-level talent programs; connect labeling to measurable fraud prevention outcomes; deliver actionable insights to Product, R&D, Analytics, and GTM; and leverage AI and automation to increase productivity and business impact.

Partner with functional leaders to translate business strategy into people strategy. Own org design, performance management, compensation, talent development, engagement, and retention. Provide coaching on career pathing, change management, and sensitive employee issues. Use people data to generate insights and recommendations, build compliant HR policies, manage global employee relations, and collaborate with People Team COEs to deliver cohesive people programs at scale.

Serve as second-in-command for Coinbase Derivatives' Market Regulation Department, leading day-to-day regulatory compliance, managing exams and audits, advising on CFTC Core Principles, reviewing surveillance escalations and enforcement actions, and coordinating cross-functional regulatory workstreams to ensure the DCM meets obligations and resolves findings timely.

Oversee global analytics and algorithm design initiatives for a nonprofit: identify high-impact projects, coordinate with student directors, collect feedback, design and run training for developers and technical advisors, and form partnerships to support implementation. Volunteer commitment ~5-6 hours/week.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Provide legal guidance on Rubrik's AI use and products, ensuring compliance with laws and contracts, drafting and negotiating AI terms, coordinating generative AI review processes, educating employees, and improving AI review controls and workflows across cross-functional teams.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Drives operational excellence through data analysis, dashboards, process improvements, and cross-functional project leadership. The role embeds with business units to manage day-to-day operations, investigate trends, answer strategic questions, implement workflows, coordinate stakeholders, track deliverables, and present insights to technical and non-technical audiences. It serves as a strategic partner to operations leadership in a fast-paced, ambiguous environment.

Lead complex R&D and scientific AI engagements from pre-sales through production delivery. Partner with executives, researchers, engineers, and product teams to define use cases, architectures, backlogs, MVPs, simulations, integrations, data pipelines, and success metrics. Assess technical feasibility, coordinate multidisciplinary workstreams, and develop reusable architectures and delivery playbooks. Apply AI, machine learning, cloud computing, scientific workflows, and HPC to transform research processes while influencing product roadmaps and scaling solutions across industries.

Own the technical sales cycle for Databricks consulting engagements, including discovery, architecture design, solution scoping, estimation, proposals, demos, proofs of concept, workshops, and statements of work. Design lakehouse, data engineering, analytics, governance, security, and AI architectures. Partner with sales, Databricks, alliance, and delivery teams while ensuring effective handoffs into implementation.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

Lead development and maintenance of character creation, rigging, animation, and simulation tools for game production. Collaborate with artists, engineers, studios, and research teams to prototype workflows, integrate game engines, optimize environments, and apply AI/ML to improve development efficiency. Build and customize internal tools, automate project environments, troubleshoot production issues, document systems, and support varied client pipelines. Participate in internal demonstrations and conferences, with required travel.

Develop and support internal low-code applications and AI agents using Microsoft Power Platform and Copilot Studio. Assist with API and database integrations, automated testing, Agile delivery, intelligent automation, documentation, and application monitoring. Collaborate with cross-functional teams to create secure, compliant, reliable digital solutions that improve enterprise productivity and workflows. The role is remote with occasional travel to Vermeer campuses.

Lead and own cross-organizational technical programs for ML and robotics platform development, define roadmaps and milestones, remove blockers, align diverse engineering teams, and drive adoption of platform best practices to deliver scalable autonomy and infrastructure outcomes.

Define and execute the market strategy for UserTesting’s AI solutions. Own positioning, messaging, value propositions, go-to-market plans, sales enablement, customer and buyer research, competitive differentiation, thought leadership, and market feedback loops. Translate complex AI workflows and human-judgment applications into compelling solutions for technical enterprise buyers, while influencing product and company strategy through cross-functional leadership.

Manage strategic enterprise accounts across a national territory, expanding existing customers and acquiring new logos. Build consultative account strategies, serve as a trusted AI and customer experience advisor, develop customer and channel-partner relationships, forecast and exceed bookings targets, and coordinate cross-functional teams to maximize customer and business outcomes. Support initiatives across the customer lifecycle, including business development, messaging, commercial positioning, and operational effectiveness.

Leads a team of machine learning engineers and data scientists developing and deploying styling algorithms and personalized client experiences. Owns AI and ML strategy, product roadmap contributions, experimentation, production deployment, code quality, and data practices. Partners with product, design, merchandising, styling, and business teams to optimize personalization outcomes while coaching the team and fostering collaboration, innovation, and technical excellence.

Lead delivery of medium- and large-scale AWS programs, overseeing planning, assurance, reporting, risk mitigation, program health, and customer outcomes. Manage multi-year product and service roadmaps, identify business opportunities, resolve customer issues, and drive continuous process improvement. Lead, recruit, coach, and develop business analysts and engineers while collaborating with technical and business leaders across global enterprise clients.

Develop machine learning and AI models using clinical, claims, and social determinants of health data to improve pediatric patient outcomes. Partner with stakeholders and cross-functional teams to define requirements, optimize data collection, build data assets, conduct exploratory analysis, and deliver strategic and patient-care insights. Query Snowflake independently and present findings to nontechnical staff.

Develop, test, maintain, and improve software solutions using established development practices. Write clean code, assist with feature implementation, debug issues, and collaborate remotely with team members. Support AI technology integration, automation, and AI-powered development workflows. Research emerging AI tools, document technical processes, and continuously develop programming skills.

Own lifecycle marketing performance across patient onboarding, activation, early care, and retention. Design and analyze multichannel experiments, build reporting and dashboards, write SQL, diagnose funnel and segment performance, and integrate AI and machine learning signals into lifecycle journeys. Collaborate with Product, Operations, Data Engineering, Marketing Operations, Analytics, and Creative to improve patient outcomes and measurable business performance.

Leads product strategy, roadmap development, prioritization, product metrics, and go-to-market execution for a marketing planning and analytics platform. Manages and coaches product managers while acting as an individual contributor on key initiatives. Partners with the CEO, engineering, sales, marketing, and leadership teams to deliver API-based, data-heavy statistical and machine learning products for technical B2B SaaS customers.

Leads enterprise cybersecurity architecture for AI, cloud, hybrid, and on-premises environments. Designs secure AI architectures, cloud foundational controls, zero-trust identity models, API and data protections, and secure LLM, RAG, agent, and MCP integrations. Establishes governance, reusable security patterns, model-usage procedures, compliance controls, and audit readiness across AWS and Azure. Advises executives and technical teams, mentors security professionals, and drives secure cloud migrations while addressing AI/ML risks such as adversarial attacks, model poisoning, data drift, bias, and data leakage.