Top Machine Learning Jobs in Greensboro, NC

As an AI Solution Engineer, you will architect and demonstrate AI solutions, lead technical discussions, and assist customers with AI outcomes in pre-sales engagements.

Build and scale core components of an end-to-end proteomics software stack (LIMS, data pipelines, web portal). Design agentic backends and AI-augmented tooling, develop analysis algorithms and lab integrations, deploy cloud IaC and CI/CD, write tests and monitoring, and collaborate with R&D, operations, and customers to productionize tools for high-throughput proteomics.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

The Principal Product Manager - Growth will lead growth initiatives, focusing on user engagement, retention, and the use of AI to enhance product strategies and experiences.

The Senior Technical Architect will lead AI SDLC transformations, create architectural solutions, mentor teams, and drive innovation in regulated industries.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

As a Regional Sales Director, you'll lead a sales team, develop business plans, and drive sales growth by managing complex cycles and nurturing talent.

Lead technical strategy and build the initial product: architect reliable, auditable AI-driven workflow systems, work with design partners, recruit and lead engineering, and partner on fundraising and company strategy.

The role focuses on strategic business partnership, forecasting, process optimization, and data insights for Zendesk's Global Renewals organization, enhancing retention and revenue outcomes.

Design and scale fraud-mitigation strategies for buyer, seller, and account fraud across Stripe products. Monitor portfolios, identify and predict risky behavior, propose controls, automate workflows, collaborate with Product/Engineering/Data Science, and educate users to minimize losses while enabling growth.

Lead product lifecycle for internal PaaS and Data-as-a-Service offerings focused on SRE and data engineering. Drive system reliability (99.99% availability), scaling strategies, GCP cost optimization, BigQuery/table and ETL performance, AI/ML enablement, and data governance/compliance across cross-functional teams.

Lead credit and funding decisioning for auto lending partners, exercising delegated lending and funding authority, resolving exceptions and stipulations, feeding performance insights to product and risk teams, conducting win/loss analysis, and enabling dealer-facing operations and training to protect portfolio performance and improve approval workflows.

Manage a portfolio of enterprise and mid-market North American customers to drive adoption, retention, expansion, and measurable ROI for Jumio's identity verification SaaS. Monitor health signals, build success plans, lead business reviews, partner cross-functionally, handle escalations, and ensure customers achieve outcomes tied to renewals and expansion.

Lead Buildertrend's end-to-end payments risk and operations: merchant onboarding, underwriting, transaction monitoring, disputes, chargebacks, and portfolio management. Build AI-assisted fraud prevention, scale automation, embed risk controls with Product and Engineering, develop analytics and governance, and grow a high-performing team to balance risk and business growth.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

The AI Sales Executive drives new business growth and expands client relationships by selling AI-driven solutions for engineering and manufacturing sectors, managing the full sales cycle with a consultative approach.

Own and implement end-to-end product features across the stack (React/TypeScript front end, Ruby on Rails backend, relational DBs, GCP infra). Design APIs and data models, ensure performance and reliability, integrate AI/LLM-powered features, collaborate with product/design/clinical teams, perform code reviews, and mentor junior engineers.

Own Oscilar's AML product suite (transaction monitoring, KYC/KYB, screening, case management, SAR filing, AI agents). Define detection rules, model calibration, data strategy, and defensible audit trails. Collaborate with engineers and compliance, use AI across discovery/design/analysis, deliver PRDs, validate models, and ship production features.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Lead and scale a global Deal Desk to structure complex SaaS transactions, own deal review and approval workflows, improve quote-to-cash across CRM/CPQ/CLM/billing/ERP, implement AI/ML for efficiency, provide analytics on discounting and deal quality, and build a high-performing global team while advising Sales and executives on strategic deals.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.