Top AI & Machine Learning Jobs

Leads Slate’s GenAI strategy, architecture, and production deployment across vehicle engineering, manufacturing, supply chain, software, and go-to-market operations. Owns enterprise-scale GenAI platforms, agentic systems, data and context layers, model serving, evaluation, guardrails, and observability. Applies physical AI to robotics, computer vision, predictive maintenance, and factory feedback loops. Recruits and develops a high-performing technical team while remaining hands-on with coding, architecture, stakeholder engagement, and technical standards.

Build ECP’s platform organization and AI foundation, including agent tool surfaces, retrieval and context layers, model-serving paths, evaluation systems, and customer-facing agent workflows. Own shared identity, data, messaging, and event-driven services used across teams. Establish reliable interfaces between legacy ColdFusion and newer services, implement contract testing, optimize data performance, and support production systems handling governed clinical data in a multi-tenant HIPAA SaaS environment.

Design and build production LLM infrastructure, including orchestration, prompting, workflow layers, retrieval integrations, MCP tools, evaluation systems, dashboards, and observability. Improve platform reliability through caching, incident response, scalability, and model rollbacks. Establish architecture standards, conduct design reviews, mentor engineers, and communicate AI concepts to nontechnical stakeholders.

Build and harden Nava’s shared AI platform for LLM-based products. Responsibilities include reusable context and agent-state management, safe long-running agent execution, sandbox integration, model routing, subagent orchestration, token-cost optimization, tracing, evaluation, testing, and documentation. The role partners with product teams to create maintainable infrastructure that supports production AI features at scale.

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.