Policy Research, Strategic Advisory
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93 open AI policy & safety jobs at 42 employers, updated twice a day.
As of 23 September, 93 AI policy & safety roles are open at 42 employers. 5 new roles have been posted since 21 September. The countries with the most are the United States, the United Kingdom and India. Of the roles that say how they work, 35% are remote or hybrid.
Among the roles listed here, AI policy, safety and trust openings are concentrated heavily in the United States, which accounts for nearly three quarters of postings. The United Kingdom and India each represent a smaller but notable share, with a handful of other countries including Germany, Canada and Ireland also appearing. The spread of seniority is striking: manager, lead and director levels together account for the large majority of stated seniority, while junior and intern roles make up a small minority. Most openings were posted within the past month, and a meaningful share appeared in the last week alone. Employer concentration is moderate, with about a third of roles sitting at the five largest hiring organisations. There is not yet enough weekly history to describe a trend over time.
Figures are measured every Monday.
AI policy and safety work spans the practices organisations use to identify, measure and reduce harms arising from AI systems, and to meet regulatory and ethical obligations around their deployment. It sits at the intersection of technical and governance disciplines, drawing on frameworks such as the NIST AI Risk Management Framework, which organises work around governing, mapping, measuring and managing risk, and the EU AI Act, which imposes obligations scaled to the risk level of a given AI use. ISO/IEC 42001:2023 provides an international management-system standard for organisations developing or deploying AI. Practitioners often come from privacy, legal and compliance, or digital-governance backgrounds, and the function has not yet settled into a single team in most organisations.
Evaluation is the most commonly mentioned skill in role descriptions on the board, followed by Python and familiarity with large language models. Agents and APIs appear in a meaningful share of descriptions, suggesting some roles require hands-on technical work alongside policy thinking. Reinforcement learning and fine-tuning are minority mentions, pointing to more research-oriented positions. Advanced academic credentials and publications feature in roughly one in ten descriptions, indicating a subset of roles that lean toward research or external-facing technical credibility.
Share of the open roles' descriptions that mention it, from a sample of 93. A mention is not a requirement.
On-site work is the dominant arrangement among roles that state a work mode, accounting for nearly two thirds. Hybrid and remote roles each represent a small but comparable share, together making up just over a third. This on-site weighting is notably higher than in many other AI disciplines, possibly reflecting the compliance and stakeholder-facing nature of much of the work.
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