Hex Offices

Hex is headquartered in San Francisco and has 2 office locations.

Hybrid Workplace

Employees engage in a combination of remote and on-site work.

We have a truly hybrid workforce, with many employees working fully remotely, and others coming into one of our two offices (SF or NYC) 2-3 days per week.

Typical time on-site: Flexible

U.S. Office Locations

HQ

San Francisco

330 Jackson Street, San Francisco, CA, United States, 94111

New York

44 W 18th St, New York, NY, United States, 10011

Recently posted jobs

3 Days AgoSaved
Hybrid
San Francisco, CA, USA
Artificial Intelligence • Big Data • Software • Analytics • Business Intelligence • Big Data Analytics
Own and improve Hex’s corporate IT infrastructure, including identity, endpoints, SaaS applications, employee lifecycle systems, office technology, and asset management. Build automation and AI agents, manage macOS fleets, strengthen monitoring and security practices, resolve complex operational issues, and partner with Security, People, Finance, Engineering, vendors, and consultants. The role is hands-on and expected to progressively assume day-to-day leadership of the IT function.
4 Days AgoSaved
Hybrid
2 Locations
Artificial Intelligence • Big Data • Software • Analytics • Business Intelligence • Big Data Analytics
Lead Hex’s talent function as the company scales from 220 to 500 employees. Own talent strategy, hiring standards, interviewer training, sourcing innovation, recruiting technology, employer value proposition, candidate experience, compensation discussions, and difficult searches. Manage recruiters, sourcers, coordinators, and recruiting operations while personally executing searches. Build scalable, data-driven systems using automation and AI to improve hiring quality, speed, and consistency.
4 Days AgoSaved
Hybrid
San Francisco, CA, USA
Artificial Intelligence • Big Data • Software • Analytics • Business Intelligence • Big Data Analytics
AI Engineer Interns will research, design, evaluate, and productionize AI-powered data capabilities. Responsibilities include running AI/ML experiments, developing agent self-improvement and continuous-learning systems, collaborating with product engineers, building evaluation and training infrastructure, optimizing performance, designing APIs, and contributing across the full development lifecycle.