Top Machine Learning Jobs in Greensboro, NC

Leads Experian Health’s Patient Financial Experience portfolio, setting multi-year product strategy, investment priorities, roadmap, and P&L accountability. Oversees patient scheduling, estimates, payments, financial clearance, statements, charity care, and collections products. Builds and develops a high-performing product organization, drives AI-enabled healthcare solutions, and represents the portfolio with customers and industry stakeholders. Requires deep healthcare revenue cycle expertise, regulatory fluency, executive communication skills, and experience managing multi-product portfolios and senior product managers.

Leads the architecture, development, deployment, and production support of secure, scalable SaaS software across a public safety platform. Champions AI-native development practices, including coding agents, automated testing, code review, and agent workflows. Mentors engineers, collaborates cross-functionally, improves reliability and performance, manages incident response, and implements secure development practices for sensitive criminal justice data.

Develop, test, optimize, document, and troubleshoot distributed robotics software for agricultural and construction equipment. The role uses C++ in Linux environments and involves deployment support, system debugging, API and architecture documentation, work estimation, and collaboration with embedded software, QA, mechatronics, computer vision, and machine learning teams. Preferred experience includes sensor integration, vision systems, GPU computing on edge devices, Python robotics development, and autonomous vehicles.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Develop and advance the organization’s Insider Threat Program by assessing capabilities, creating behavioral indicators, enhancing detection analytics, and investigating suspicious activity. The role analyzes security, fraud, and behavioral telemetry; configures monitoring technologies; reduces false positives; produces risk reporting and metrics; and partners with Cybersecurity, Fraud, HR, Legal, Risk, and Technology teams to improve insider-risk detection and response.

Lead research at the intersection of AI, cybersecurity, and computer systems. Define research problems, develop hypotheses, build prototypes, design rigorous experiments, evaluate AI and security methods, and translate findings into practical systems. Research areas may include AI agents, autonomous penetration testing, attack-path reasoning, cyber deception, AI safety, and security evaluation. Mentor junior researchers, collaborate with engineering and product teams, and communicate findings through reports, presentations, prototypes, or publications.

Independently lead complex technology and commercial transactions, including SaaS, cloud, licensing, vendor, partnership, and data-processing agreements. Advise cross-functional stakeholders on privacy, security, intellectual property, GDPR, AI, regulatory, and commercial matters. Improve contracting through templates, playbooks, CLM workflows, AI-enabled tools, analytics, and scalable legal operations. Analyze legislation and regulatory developments affecting cybersecurity, privacy, AI, and digital infrastructure.

Owns the commercial health and client relationships for Databricks data and AI engagements. Responsibilities include managing budgets, margins, forecasts, scope changes, executive stakeholders, steering committees, delivery transitions, business outcomes, close-outs, and follow-on opportunities. Partners with Sales, project managers, solutions architects, and technical leaders to deliver measurable client value across data engineering, lakehouse, governance, BI, and AI initiatives.

Manage the full enterprise sales cycle, from prospecting and pipeline development through deal closure and account expansion. Build relationships with executives and decision-makers, run multichannel campaigns, develop pursuit strategies, coordinate with partners and internal teams, maintain accurate forecasts, and achieve revenue targets for data engineering, platform modernization, and AI/ML consulting services.

Drive the full enterprise sales cycle for data engineering, platform modernization, and AI/ML consulting services. Build pipeline through prospecting, outreach, networking, campaigns, and events; develop pursuit strategies; close complex deals; and expand strategic partnerships. Own executive client relationships, coordinate sales, delivery, customer success, solutions engineering, and marketing, while maintaining accurate forecasts and achieving revenue targets.

Represents Snowflake to senior marketing and advertising leaders in regulated industries. Develops privacy-safe measurement, identity resolution, audience activation, personalization, AI, and data clean room solutions on Snowflake. Leads strategic customer engagements, creates reference architectures and reusable playbooks, partners with sales, product, engineering, and technology partners, and serves as a marketing and AI thought leader through workshops, enablement, webinars, and industry events.

Leads strategic engagements with CMOs and marketing executives across technology, media, and manufacturing. Translates marketing measurement, audience strategy, personalization, identity, and advertising challenges into Snowflake data and AI solutions. Builds demonstrable use cases, repeatable sales and enablement assets, and thought leadership for customers, field teams, and partners. Collaborates with sales, solution engineering, product, and ecosystem partners while advising executives and supporting strategic growth.

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.

Own the strategy, roadmap, backlog, and delivery of GridUnity’s AI platform capabilities. Partner with customers, engineering, design, data science, sales, and leadership to validate AI use cases, define requirements, manage experiments and releases, and measure model and business outcomes. Ensure responsible AI practices including explainability, auditability, privacy, monitoring, human review, and fallback behavior. Provide technical product expertise, support customer-facing activities, and mentor other product owners.

Supports the full AI development lifecycle by helping execute pilots and proofs of concept, documenting standards and playbooks, evaluating AI use-case pipeline health, researching tools and platforms, developing acceleration dashboards, supporting value assessments, and preparing leadership updates. The intern will also present a final project and gain experience with responsible AI, governance, and enterprise AI delivery.

Drive new enterprise business for Automation Anywhere’s Agentic Process Automation platform. Responsibilities include prospecting, managing sales pipelines, conducting customer presentations and demonstrations, attending industry events, leading proof-of-concept exercises and RFX responses, and coordinating solution design with technical teams. The role requires consultative enterprise software selling, quota attainment, technical communication, and collaboration with sales engineers, partners, and professional services.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

Lead growth data science initiatives by partnering with Marketing, Sales, and Customer Experience leaders. Develop analyses, experimentation frameworks, and machine learning models for acquisition, engagement, retention, monetization, targeting, segmentation, uplift modeling, and lifetime value prediction. Productionize reliable models with engineering teams, communicate insights to senior stakeholders, establish experimentation best practices, and mentor junior data scientists. The role requires strong business judgment, advanced technical expertise, and experience operating in ambiguous, zero-to-one environments.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.