Top Artificial Intelligence Jobs in Raleigh-Durham, NC

The Enterprise Account Executive will manage full-cycle enterprise deals, targeting mid-market to enterprise DTC brands, executing end-to-end sales processes, conducting demos, and consulting with stakeholders.

Own and ship developer-focused observability product features (alerts, APM, onboarding, connected investigations). Collaborate with engineers and power users to improve UX, reduce alert fatigue, speed root-cause workflows, and optimize telemetry onboarding and query performance.

Own the full sales cycle for enterprise accounts across the US: prospect, build pipeline, engage C-level security and DevSecOps leaders, map product to business outcomes, and close complex multi-stakeholder deals while collaborating with sales, marketing, and customer success.

As an Account Executive, you will own the sales cycle, prospect for enterprise customers, and close significant deals in cybersecurity, focusing on high-value business outcomes.

The Mid-Market Account Executive will develop customer relationships and manage sales pipelines for companies with $100M - $500M revenue, achieving quarterly quotas and collaborating cross-functionally.

This role involves selling enterprise-grade SaaS solutions to the Health Insurance sector, managing C-level relationships, strategizing territory plans, and identifying new opportunities within the Healthcare sector.

This role involves leading a perception team in developing algorithms for autonomous driving systems, supervising team members, and conducting research to enhance perception capabilities.

The Senior Backend Data Engineer will extend data ingestion and processing pipelines, build APIs and backend services, and provide technical leadership.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

Lead full‑stack engineering projects across frontend and backend, design system architecture, collaborate with ML researchers and clinicians, implement user‑facing features, define APIs, write tests (unit/integration/e2e), produce technical specs, and use cloud infrastructure and CI to ship iteratively.