Global Candidate Experience Manager

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About the Team

The Candidate Experience team ensures that every interaction candidates and interviewers have with OpenAI reflects excellence, fairness, and humanity. This team owns global coordination, interview logistics, onsite experience, Interview Programs, and the operational workflows that support all recruiting functions, from Executive and Research to Technical, GTM, and G&A. The Candidate Experience team supports a wide range of hiring modalities (from executive and research to high-volume technical roles), and we need someone who understands how to adapt across them. As OpenAI scales globally, this team plays a critical role in designing experiences that are both bespoke for highly specialized searches and scalable across rapidly growing teams and offices.

About the Role

As Global Candidate Experience Manager, you will manage CE managers and Interview Programs program managers, with global responsibility for candidate experience, coordination and interview program delivery. You will lead through the team’s managers, leads and program owners while staying close enough to daily work to understand where it breaks. You will own service quality, capacity planning, interviewer readiness, program delivery and continuous improvement across high-volume and highly bespoke hiring.

We are looking for a hands-on operating leader who can build useful AI-enabled workflows, forecast and allocate capacity, and drive complex programs from diagnosis through launch and sustained adoption. You should be comfortable reviewing a queue with coordinators, testing a workflow with technical partners, and translating a broad goal into clear owners, milestones and decisions. Your aim is to strengthen the team’s judgment and autonomy while improving candidate care.

You can move between global operating design and frontline detail, reprioritize as demand changes, and deliver high-quality work quickly despite ambiguity. You know when to standardize and automate, when a human should remain accountable, and how to explain practical tradeoffs among speed, quality, workload and fairness.