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White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems through natural-language policies that define what AI models should and shouldn't do. The company has raised $11M from top-tier investors and processes over 100 million API calls monthly, with a team that fine-tunes and trains its own LLMs.
As an AI Research Recruiter, you will own full-cycle hiring across research, machine learning, data, evaluations, and ML infrastructure roles. You'll develop deep understanding of White Circle's research agenda and technical challenges, partnering closely with research leadership and the CEO to define specialized candidate profiles. Your responsibilities include sourcing talent from AI labs, research organizations, technical startups, universities, open-source communities, and scientific networks; understanding the distinctions between Research Scientists, Research Engineers, Applied ML Engineers, and ML Infrastructure Engineers; and evaluating candidates' actual technical contributions.
You'll engage passive candidates—researchers and engineers not actively job-seeking—and communicate why White Circle's problems are technically meaningful. You must stay current with developments in AI safety, model behavior, evaluations, multimodal systems, agentic systems, data quality, post-training, and ML infrastructure. You'll design structured hiring processes that assess research depth, engineering ability, originality, and real-world impact, while delivering exceptional candidate experience for those navigating multiple competitive opportunities.
Key qualifications: 2+ years full-cycle recruiting experience with significant hiring of Research and ML talent; personal experience hiring for Research Scientist, Research Engineer, ML Research Engineer, Applied Scientist, Multimodal ML Engineer, or ML Infrastructure Engineer roles; ability to discuss LLM training, fine-tuning, inference, evaluations, data pipelines, and model behavior; skill in interpreting technical profiles beyond keywords and assessing publication significance, open-source work, and production systems; knowledge of where exceptional AI research talent can be found and how to reach non-obvious candidates; participation in AI research and engineering communities; ability to build credibility with highly technical candidates; experience recruiting across US and European markets; fluent English (C1+).
Pluses include experience recruiting for frontier AI labs, AI safety organizations, or leading AI companies; strong existing network among AI researchers and ML specialists; experience sourcing through publications, conferences, GitHub, and academic labs; technical background in computer science, machine learning, or mathematics; and early-stage startup recruiting experience.
