Top Machine Learning Jobs in Greensboro, NC

Tests and validates AI-powered products, machine learning models, and Generative AI applications. Designs test strategies, executes functional, regression, and user acceptance testing, validates model outputs, identifies defects, analyzes quality metrics, documents evidence, and supports compliance and release readiness. Collaborates with AI engineers, product managers, and business teams to improve testing methodologies, quality frameworks, and AI evaluation practices.

Build and deploy AI agents, intelligent applications, scalable backend services, APIs, and full-stack solutions. Integrate large language models, AI services, data platforms, and external APIs into customer-focused products. Collaborate with engineering, product, and cross-functional teams while contributing to cloud-native applications, data pipelines, distributed systems, testing, deployment, and production operations.

Designs, implements, optimizes, and administers generative and agentic AI solutions. Translates business requirements into AI workflows, oversees model deployment and performance, analyzes implementation data using Power BI, and coordinates responsible AI governance. Collaborates with engineering, security, data science, sales, and reporting teams while supporting users and maintaining AI platforms, cloud services, and low-code tools.

Owns intake and triage for marketing technology requests, learns marketing tools and data flows, and designs automation workflows with engineers and data scientists. Builds solutions using internal frameworks and low-code tools, tests variable AI outputs, guides user acceptance testing, documents processes, enables marketing teams, and measures business impact. The role connects marketing needs with agentic workflows, integrations, custom applications, and ML-based analysis.

Own the technical systems that convert market signals into revenue pipeline. Build and maintain prediction models, multichannel campaign infrastructure, AI-driven personalization, call intelligence, and signal-to-task workflows. The role requires production coding, applied machine learning, API integrations, SQL, CRM and GTM stack expertise, and practical LLM usage. You will manage live revenue systems, improve attribution and data quality, and balance rapid experimentation with reliability.

Analyst supporting a marketing engineering team by translating marketing needs into automated workflows, integrations, internal applications, and AI-enabled solutions. Responsibilities include intake and prioritization, process analysis, workflow design, low-code development, collaboration with engineers and data scientists, testing variable AI outputs, user acceptance testing, documentation, enablement, and impact measurement.

Develop and maintain quantitative models for forecasting, pricing optimization, and risk assessment. Analyze mortgage business metrics to identify revenue, profitability, and operational improvement opportunities. Build performance measurement frameworks, dashboards, automated reports, and SQL-based analytics workflows. Use Python for statistical analysis, automation, and machine learning workflows. Validate and monitor model performance, recalibrating models as needed, while communicating actionable recommendations to business leaders.

Deploy onsite with strategic customers to understand security and risk workflows, then build AI-powered integrations, data pipelines, and production software. Translate security ratings into financial risk models, connect systems across cloud environments, rapidly prototype and harden solutions, and guide technical relationships through renewal and expansion. The role bridges customers, Product, Engineering, Customer Success, and Sales Engineering while feeding field insights into the product roadmap.

Own the strategy and roadmap for Constructor’s search quality and Product Discovery Engine. Drive measurable improvements in relevance, conversion, revenue per visit, and GMV across retail verticals. Partner with ML, data science, and engineering teams to productionize state-of-the-art retrieval, ranking, query understanding, personalization, and LLM capabilities. Establish evaluation frameworks, investigate quality issues, align multiple teams, and expand into chapter-wide product leadership.

Develop statistical, machine learning, and deep learning models to measure the impact of pharmaceutical HCP, DTC, and Medical Affairs campaigns. Partner with internal and external clients, provide analytical insights and methodological guidance, consult with sales and market research teams, and coach an offshore analytics team. The role requires applying causal inference, healthcare and pharmaceutical data expertise, and clear communication of findings to clients and executives.

Leads the Intelligence Group’s product strategy across ML optimization, AI targeting, identity resolution, taxonomy, data partnerships, audience products, and campaign forecasting. Manages product managers, prioritizes AI/ML initiatives, oversees data vendor integrations, and translates complex data science capabilities into commercial advertising products. Partners with engineering, sales, executives, and clients while driving platform modernization and healthcare programmatic advertising innovation.

Leads the full lifecycle of clinical NGS diagnostic algorithms for oncology and liquid biopsy applications. Responsibilities include algorithm design, analytical validation, design control, regulatory readiness, variant calling, QC metric development, probabilistic modeling, machine learning, and validation pipeline automation. The role serves as technical authority for algorithm changes, supports regulatory submissions, and develops solutions for low-input, fragmented, and ultra-low-frequency genomic variants.

Analyzes, designs, develops, and maintains Java-based eCommerce systems, web applications, payment-processing integrations, APIs, and distributed infrastructure. The role also builds AI and machine-learning tools for transactional and operational data analysis. Responsibilities include architecture evaluation, performance tuning, security and PCI DSS compliance, testing, documentation, CI/CD maintenance, troubleshooting, and Agile collaboration.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

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.

Lead ecosystem-level data science and econometric analyses across Xbox, Game Pass, Console, PC, and Store experiences. Evaluate growth, engagement, monetization, experiments, incrementality, and investment ROI using statistical and causal inference methods. Build reusable analytical products and self-service capabilities, influence executive decisions, and mentor team members.

Evaluates and compares AI-generated responses in English and French for accuracy, relevance, reasoning, clarity, safety, cultural appropriateness, and overall usefulness. Applies detailed annotation guidelines consistently across varied content types, documents evidence-based decisions, meets daily productivity expectations, and participates in training, calibration, qualification, and quality reviews.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Research agentic AI use cases for customer-facing fintech features and develop working prototypes. Design, build, and productionize advanced models while collaborating with Product, Engineering, Analytics, and Applied Science teams. The intern will contribute to customer experience, operational efficiency, and business outcomes in an eight-week, full-time program based in New York City.

Define technical vision and architecture for agentic, multi-agent systems that improve autonomous-driving event triage and evaluation. Lead development of scalable frameworks supporting dynamic reasoning, planning, tool use, self-reflection, and orchestration. Identify applications across the Waymo Driver evaluation lifecycle, establish roadmaps, collaborate across teams, and mentor engineers. The role requires extensive software engineering and AI/ML expertise, technical leadership, and experience building complex production systems.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Senior backend engineer responsible for setting technical direction, designing scalable distributed systems, building customer-facing web applications, mentoring engineers, and applying machine learning to large-scale healthcare data. The role requires Java, Scala, or Rust expertise, cloud systems experience, technical design leadership, cross-functional collaboration, and adoption of agentic engineering practices.

Leads Alkami’s enterprise and technology risk management program, including risk assessments, control evaluations, mitigation, monitoring, reporting, and escalation. Owns AI, machine learning, and generative AI governance, translating regulatory and risk expectations into practical policies, controls, and review processes. Partners with cross-functional risk owners and supports compliance initiatives involving business continuity, disaster recovery, regulatory management, and incidents. Reports to the Chief Compliance Officer in a fully remote role.

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.