Top AI & Machine Learning Jobs

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.

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.

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.

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.

Provides technical leadership for AI engineering and SDLC automation across a platform engineering organization. Designs and delivers AI agents, copilots, developer tools, and automated workflows integrated into CI/CD pipelines and engineering platforms. Establishes responsible AI governance, safety guardrails, evaluation methods, and secure integration patterns. Leads cross-functional initiatives, mentors engineers, drives adoption, and measures improvements in productivity, quality, reliability, and developer experience.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Build customer-facing AI features and horizontal infrastructure to deliver trustworthy, auditable AI automation for financial accounting. Own full-stack feature development, measure and improve AI product efficacy, and make strategic product and technical decisions.

Build production AI and agentic workflows that combine physics-informed models, LLMs, VLMs, scientific data, retrieval, memory, and deterministic rules. Develop secure, durable systems with evidence-backed outputs, permissions, auditability, human approval, and recovery capabilities. Evaluate performance for task success, scientific validity, safety, latency, cost, and human intervention while partnering with product and design teams to deliver customer-facing intelligence tools for operational decision-making.

Build and deploy LLM-powered agents, skills, and platform capabilities across the company. Own the full lifecycle from design and implementation through evaluations, rollout, and ongoing improvement. Define specifications and measurable quality evaluations, partner with the Applied AI product manager, make independent product decisions, and collaborate with sales, revenue operations, editorial, and people operations to identify and automate workflows.