Top AI & Machine Learning Jobs

Develop and maintain statistical and machine learning models supporting credit risk, fraud detection, customer segmentation, marketing optimization, and transaction monitoring. Analyze and optimize financial crime detection models, monitor performance and bias, manage model data, and collaborate with risk, compliance, technology, operations, and validation teams. Lead analytics capability development, mentor colleagues, evaluate emerging AI technologies, support transformation initiatives, and communicate complex model risk and performance findings to stakeholders.

Summer 2027 internships provide hands-on experience across data analytics, business intelligence, data engineering, data science, machine learning, AI, and data platforms. Interns may analyze and prepare data, develop dashboards and pipelines, improve data quality and governance, support predictive models, and apply AI to business problems. Assignments are onsite in Winston-Salem, North Carolina, with technologies and responsibilities determined by business needs.

Supports cross-functional AI adoption by identifying use cases, coordinating initiatives, translating business needs into AI workflows, and promoting AI understanding. Explores emerging AI tools, recommends innovative applications, and helps implement solutions that improve customer experience, operational efficiency, and decision-making. The intern gains hands-on exposure to machine learning, large datasets, modern AI tools, and data-driven business challenges while collaborating with technical and non-technical stakeholders.

Owns the vision, roadmap, stakeholder alignment, and continuous improvement of AI and intelligent automation solutions in healthcare. Collaborates with engineers, data scientists, and project managers to translate requirements into user stories, facilitates Agile ceremonies, conducts market and competitive research, monitors solution performance, and communicates progress to stakeholders. Requires strong analytical, communication, and leadership skills along with experience managing AI solutions.

Lead AI engineering teams developing scalable agentic AI solutions. Responsibilities include technical leadership, architecture and implementation of AI agents, Generative AI and RAG integrations, production model reliability, code quality, delivery planning, troubleshooting, mentoring, and evaluating new technologies. The role requires machine learning experience with TensorFlow, PyTorch, and scikit-learn, cloud and MLOps expertise, and strong collaboration skills.

Design, build, evaluate, and deploy AI agents and machine learning automation for engineering, product, delivery, and customer-facing workflows. Integrate AI into existing software, collaborate with technical and healthcare stakeholders, optimize systems for accuracy, latency, cost, and safety, and document development processes. The role emphasizes LLMs, agentic frameworks, production ML systems, human-in-the-loop safeguards, and healthcare-aware solutions.

Paid AI and intelligence intern supporting machine learning model development, AI agent training, data preparation, predictive analytics, NLP, AI dashboards, and custom intelligence solutions. The role involves researching emerging AI techniques, documenting model performance, and collaborating with strategists, policy experts, and creative teams on advocacy, reputation, and policy-related client work.

Leads and scales engineering for an enterprise Agentic AI platform serving healthcare payers and providers. Owns technical strategy, architecture, infrastructure scalability, security, compliance, DevSecOps, release execution, cost optimization, and customer deployment readiness. Builds high-performing global teams, partners with Product, CTO, Sales, and Customer Operations, and delivers resilient AI products supporting clinical workflows, interoperability, analytics, and enterprise integrations.

Build and deploy AI systems for leading labs and enterprise partners. Responsibilities include designing data intelligence platforms, ML and evaluation pipelines, taxonomies, labeling workflows, LLM applications, multi-agent and RAG systems, inference infrastructure, and human-in-the-loop workflows. The engineer will translate ambiguous research needs into production systems, manage full technical lifecycles, and collaborate directly with researchers, founders, and strategic partners.

Architects and builds production-grade AI systems for enterprise clients, owning the full journey from discovery and system design through hands-on engineering, deployment, scaling, observability, and production hardening. Leads LLM, RAG, agentic, data pipeline, and infrastructure architecture; engages senior client stakeholders; drives go-live outcomes; establishes reusable AI patterns; researches emerging frameworks; and mentors engineers.