Top Machine Learning Jobs in Greensboro, NC

Leads enterprise technology strategy, architecture, modernization, security, reliability, AI adoption, and execution. The VP will oversee engineering and data organizations, develop technical leaders, establish roadmaps and performance standards, guide cloud and data platform evolution, and connect technology investments to measurable business outcomes. This executive partners with the CEO and cross-functional leaders while managing organizational performance, operational risk, governance, resilience, and technology investments.

Lead the demand generation function by translating go-to-market priorities into segmented campaigns across email, search, social, content, webinars, and events. Own HubSpot automation, Salesforce integration, lead scoring, funnel analytics, marketing-sourced pipeline, budgets, and agency performance. Collaborate with Sales and Commercial Operations to improve lead handoffs and conversion. Apply AI and automation to increase execution efficiency while coaching team members and building the future demand generation team.

Drive adoption of AI-powered utility solutions through pre-sales consulting and post-sales enablement. Gather requirements, architect and demonstrate computer vision and ML workflows, lead RFI/RFP support, guide deployments and integrations, complete security audits, deliver customer training, and create technical presentations. The role requires strong AI/ML knowledge, customer-facing communication, scripting experience, and willingness to travel domestically and internationally.

Assist with building recommendation models, data pipelines, feature engineering workflows, ranking algorithms, and machine learning prototypes for personalized music discovery. Evaluate and optimize recommendation approaches, process user interaction data, and collaborate with engineering, research, and product teams to develop real-world personalization systems.

Lead the design and development of production-grade machine learning systems, including large-scale document pipelines and agentic workflows for healthcare claims and payment integrity. Drive LLM prompt engineering, multimodal AI, RAG, orchestration, observability, reliability, and explainability. Collaborate with data science, data engineering, and product teams, reduce technical debt, improve scalability, and mentor engineers on enterprise AI system design and development.

Leads new business development, account growth, commercial discussions, client relationships, and sales operations for AI and GenAI data solutions. Partners with engineering, delivery, and customer success teams to understand frontier lab needs, structure projects, close opportunities, and expand client accounts. Requires quota-based technology sales experience, startup experience, STEM education, and strong AI/ML market knowledge.

Design and scale machine learning infrastructure for personalization, search, ranking, advertising technology, and real-time learning at consumer scale. Build distributed systems, data pipelines, feature infrastructure, model-serving systems, and low-latency decision systems. Lead architecture and zero-to-one initiatives, partner across product and engineering teams, mentor technical leaders, improve experimentation velocity, and explore LLM applications for personalization and conversational search.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Own quality engineering for an AI-enabled decision-support system. Develop test strategy, translate acceptance criteria into automated tests, integrate coverage into CI/CD release gates, and perform functional, integration, performance, and load testing. Build evaluation sets, audit model decisions, measure errors and overrides, document release evidence, and maintain rollback criteria. Coordinate user acceptance testing and independently govern releases. The role requires strong coding, statistical literacy, audit-ready documentation, active Secret clearance, and up to 25% travel.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Build prototypes, conduct experiments, analyze large-scale data, and advance attribution capabilities from early concepts through production. Collaborate with engineering, product, analysts, and account teams to improve measurement and marketing ROI. Responsibilities include owning measurement products, applying Bayesian and marketing models, writing production code, and communicating methodologies and findings to technical and nontechnical audiences.

Lead the vision, strategy, and evolution of LTK’s creator experience and commerce products. Define creator workflows, identify AI-powered opportunities, analyze behavioral data, establish outcome metrics, and drive experimentation to improve engagement, retention, earnings, and product quality. Collaborate across Product, Engineering, Design, Data, and Creator Success while influencing senior stakeholders and guiding complex, multi-quarter initiatives.

Develop generative AI solutions using Python, AWS, LLMs, chatbots, prompt and context engineering, RAG, vector databases, and agentic AI frameworks. Build coding prompts and contexts with GitHub Copilot, Claude Code, and Amazon Q Developer to convert business stories into production code and pull requests. Design autonomous and multi-agent workflows, memory and token-management systems, caching strategies, MCPs, and real-time AI decisioning features. Auto-lending experience and A/B testing expertise are advantageous.

Owns CSL Plasma’s target-state business architecture, capability models, value streams, architecture decisions, integration standards, and technology roadmaps. Translates business strategy and regulatory requirements into architecture blueprints across digital products, while governing AI, automation, data, and integration patterns. Partners with product, engineering, enterprise architecture, security, privacy, and compliance teams to drive committed designs, architectural consistency, and migration sequencing across a complex regulated environment.

Leads engineering standards, architecture governance, platform strategy, technical quality, security and compliance engineering, and production AI governance across CSL Plasma’s digital products. Manages cross-domain technical teams, sets cloud and integration direction, owns shared engineering platforms and data architecture, and ensures reliable, scalable, regulated software delivery. The role also drives AI-assisted development, production AI operations, engineering metrics, talent development, and alignment with enterprise architecture and cybersecurity.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Collects, processes, and analyzes data from multiple sources using R, Python, and SQL. Develops reports, visualizations, analytical methods, and data-driven insights while applying statistical and introductory machine learning techniques. Collaborates with internal and external stakeholders, documents project progress in JIRA, debugs code, clarifies requirements, and communicates findings through reports and presentations.

Lead enterprise data and AI architecture across marketing, customer engagement, digital, sales, and analytics. Design scalable cloud data platforms, reusable data products, semantic models, real-time and batch architectures, governed AI capabilities, machine learning systems, generative AI, agents, recommendations, and personalization. Establish standards for governance, security, privacy, quality, lineage, and observability. Provide technical direction, lead architecture reviews and roadmaps, partner with engineering and data science teams, and mentor architects and engineers.

Lead enterprise security operations, resilience, incident response, threat intelligence, cloud security, IAM, and risk management. Serve as deputy to the CISO and primary incident commander while advising executives on cyber risk. Build and scale multidisciplinary security teams, modernize cloud-native defenses, embed security-by-design across products and AI/ML systems, and establish metrics-driven governance, automation, regulatory alignment, and business-focused security strategies.

Teaches undergraduate and graduate artificial intelligence courses in an online and blended format. Develops syllabi, lesson plans, course materials, assessments, and student feedback aligned with university objectives. Uses online technologies and course software to facilitate learning, integrates responsible AI practices, and evaluates appropriate student use of AI tools. Requires a doctorate, applied AI experience, AI-enabled software development expertise, strong communication skills, and commitment to ongoing faculty development.

Senior Product Designer responsible for creating high-craft, 0-to-1 AI recruiting and talent product experiences. The role involves visual and interaction design, rapid iteration, prototyping, design-system development, production-ready delivery, and experimentation with generative AI. Candidates should have strong Figma expertise, exceptional visual design skills, experience shipping SaaS, enterprise, or AI products, and a portfolio demonstrating creative range and execution.

Leads data engineering teams while designing and optimizing scalable batch and real-time data pipelines on cloud platforms. Responsibilities include developing PySpark and Spark workflows, orchestrating Airflow DAGs, managing cloud infrastructure, implementing CI/CD, improving data quality and performance, and mentoring engineers. The role collaborates with clients, product, architecture, and program management teams, while applying generative AI and DevOps practices to improve engineering productivity and operational efficiency.

Design the full Search experience across organic and sponsored results, including typeahead, ranking, filters, empty states, and monetization. Establish design principles and components, partner with product, research, data science, and ML engineering, and use experimentation to improve engagement, relevance, and revenue. The role requires substantial search or discovery design experience, strong product craft, consumer instincts, and comfort designing alongside AI and monetization teams.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Leads Propel America's product and technology strategy, including the Fellow Portal, AI integrations, technology roadmap, software development, Salesforce, infrastructure, data systems, cybersecurity, and vendor relationships. Manages technical staff and outsourced IT providers, oversees architecture, QA, releases, compliance, data privacy, and disaster recovery, and partners cross-functionally to translate organizational needs into scalable products and technology solutions.