Top Machine Learning Jobs in Greensboro, NC

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 the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

Leads delivery of large-scale AWS and digital transformation programs, overseeing planning, execution, reporting, risk mitigation, quality, and customer outcomes. Manages multi-functional teams of business analysts and engineers, develops product and service roadmaps, drives continuous improvement, and resolves customer issues. The role also involves people management, recruiting, coaching, business development, stakeholder communication, and aligning program portfolios with organizational strategy.

Manage a portfolio of advertisers on the Adflow commerce media platform, building relationships, monitoring budgets and campaign performance, presenting business reviews, coordinating onboarding, recommending optimization strategies, and identifying revenue-growth opportunities. The role serves as the primary client contact, collaborates with internal campaign, media operations, creative, and finance teams, and uses performance marketing expertise and analytics to drive advertiser success and long-term partnerships.

Manage a portfolio of advertisers on the Adflow commerce media platform. Responsibilities include client onboarding, campaign coordination, budget and revenue pacing, performance reporting, business reviews, strategic recommendations, optimization feedback, and identifying opportunities to grow advertising spend and channel distribution. The role serves as the primary client contact while collaborating with business development, campaign strategy, creative, media operations, and finance teams.

Leads global Customer Success for enterprise cybersecurity SaaS customers, owning onboarding, adoption, retention, renewals, expansion, advocacy, and customer satisfaction. Develops scalable CS strategies, support processes, KPIs, AI-driven workflows, adoption programs, and executive engagement frameworks. Partners with Sales, Product, Engineering, Finance, and Marketing while managing teams across the US and India. Drives GRR, NRR, product adoption, customer value realization, and referenceability.

Develop marketplace intelligence, forecasting, ranking, recommendation, personalization, and experimentation models for Airbnb’s search and marketplace products. Partner with product and engineering teams to launch AI-driven improvements, build scalable modeling pipelines, evaluate product changes, and guide technical direction. The role requires causal inference, machine learning, strong SQL and Python or R skills, cross-functional communication, and autonomous leadership of data science roadmaps.

Lead technical strategy for financial services customers by designing scalable data, AI, streaming, and analytics architectures. Own customer engagements from discovery through production deployment, advise senior technical stakeholders, drive Databricks adoption, lead competitive technical wins, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL coding, distributed data systems expertise, public cloud deployment experience, and the ability to translate complex architectures into business value.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Leads marketing and sales analytics strategy, architecture, and measurement. Builds data models, pipelines, machine learning datasets, attribution and incrementality models, and revenue-performance analyses. Advises leaders on investments, tools, account prioritization, and funnel economics. Establishes data, metric, testing, and privacy standards; partners with Marketing, Sales, Finance, Revenue Operations, Data Engineering, and IT; plans roadmaps; presents recommendations to executives; and mentors analysts and engineers.

Own product outcomes for an AI-native cloud infrastructure platform. Conduct customer discovery, frame problems, prioritize opportunities, prototype solutions, analyze product and business data, and collaborate with Design, Engineering, Sales, Customer Success, and Marketing. The role requires enterprise B2B product judgment, technical fluency across cloud infrastructure and FinOps, strong commercial awareness, AI-assisted workflows, outcome measurement, and end-to-end ownership from strategy through iteration.

Lead engineering for Fetch’s audience products, building scalable lifecycle services, predictive and lookalike model workflows, identity matching, partner and DSP activation, monitoring, governance, and access controls. Set technical direction, make architectural decisions, guide engineers, and partner with Data Science, Product, and Data Platform to move audience models from prototypes into reliable production systems.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Lead technical onboarding and enablement for contact center partners, architecting scalable Zendesk, Amazon Connect, and AWS solutions. Provide co-selling support, technical strategy, solution design, demonstrations, proof-of-concepts, training, documentation, governance, and mentorship. Collaborate with partners, sales, engineering, and vendors to drive successful implementations, security compliance, innovation, and adoption of AI, automation, and omnichannel contact center technologies.

Own the end-to-end product strategy and lifecycle for AI research products, from model development and user research through launch, pricing, and go-to-market. Partner with research, engineering, design, and business teams to define requirements, ship products, analyze metrics, run experiments, and prioritize features. The role requires strong technical or quantitative foundations, product management experience, SQL and Python fluency, knowledge of machine learning, and experience taking products from zero to one.

Own enterprise customer relationships after implementation, driving BigPanda platform adoption, measurable IT operations outcomes, renewals, and expansion. Lead technical customer sessions, health checks, executive reviews, adoption roadmaps, and value conversations. Partner with account teams on renewal risk, upsell opportunities, and technical proof-of-value engagements. Act as the customer’s internal advocate while guiding integrations, troubleshooting, and platform configuration.

Develop and deploy mineral exploration instruments, sensor systems, data acquisition methods, and proprietary software. Integrate geospatial, geophysical, geochemical, geological, and geographic data; build statistically rigorous and physics-based models; identify ore-forming anomalies; and generate 2D and 3D exploration predictions. Collaborate with data scientists, engineers, geoscientists, vendors, and external partners to design field programs, deploy sensors, reduce uncertainty, and guide exploration decisions.

Lead end-to-end technical strategy for customer accounts, design scalable data and AI architectures, and drive Databricks adoption and consumption. Advise senior technical stakeholders, lead architecture discussions, build custom solutions for competitive opportunities, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL skills, distributed data systems expertise, public-cloud deployment experience, and the ability to translate technical concepts into business value.

Own the roadmap and product decisions for data science products powered by NEXUS. Partner with data scientists, engineering, research, design, and applied AI teams to build self-service workflows, AI agents, MLOps capabilities, and predictive model monitoring. Conduct customer discovery, define requirements and success metrics, prioritize roadmap decisions using data and user evidence, and guide products from discovery through launch and iteration.

Lead product strategy for integrated hardware and software ecosystems serving the public sector. Oversee cameras, IoT, robotics, software, and AI/ML products across their full lifecycle, from concept and NPI through manufacturing, deployment, and end-of-life. Manage and scale the product organization, guide roadmap and P&L decisions with data, develop go-to-market strategies, and align engineering, supply chain, commercial teams, executives, and the Board.

Leads Kaseya’s market and competitive intelligence function, including competitor analysis, market sizing, customer segmentation, Voice of Customer, win/loss research, marketing effectiveness, and predictive analytics. Advises executives on market trends, customer behavior, competitive threats, and growth opportunities. Partners with product, sales, marketing, data science, and revenue operations, embeds insights into sales enablement, and builds a high-performing intelligence team.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes trusted metrics, partners with Finance, Product, Commercial, Engineering, and Data Engineering, and mentors scientists. The role drives adoption of governed self-service analytics and communicates assumptions, uncertainty, and business trade-offs to leadership.