Recruiter (Research)

TL;DR:

We’re looking for an AI Research Recruiter who knows where exceptional AI Researchers and engineers can be found. You’ll own full-cycle hiring across research, machine learning, data, evaluations, and ML infrastructure, helping us build one of the strongest teams in AI safety.

About us

White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale.

We’ve recently raised our Series A funding round, taking our total funding to $70M. Our investors include top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others

We process over one hundred million API calls every month

We fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model

We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need.

You will

• Own full-cycle recruitment for ML/AI Research, Multimodal ML Engineer, Research Scientist (AI Behaviours, Evals, Agentic Systems), and ML Infra roles
• Develop a deep understanding of White Circle’s research agenda, technical challenges, and the profiles required to solve them
• Source talent from AI labs, research organisations, technical startups, universities, open-source communities, and relevant scientific networks
• Understand the differences between Research Scientists, Research Engineers, Applied ML Engineers, and ML infra Engineers & assess candidates accordingly
• Evaluate candidates’ actual contributions
• Engage researchers and engineers who are not actively looking for a new role and communicate why White Circle’s problems are technically meaningful
• Design structured hiring processes that assess research depth, engineering ability, originality, and real-world impact
• Deliver an exceptional candidate experience, including for candidates navigating multiple highly competitive opportunities
• Track recruiting performance and continuously improve sourcing strategies, processes, tools, and workflows
• Support hiring outside your primary stream when business priorities require it
• You’ll fit right in if you
• Have 2+ years of full-cycle recruiting experience, including significant experience hiring Research and ML talent
• Have personally hired for several roles comparable to Research Scientist, Research Engineer, ML Research Engineer, Applied Scientist, Multimodal ML Engineer, or ML Infrastructure Engineer
• Understand what these roles do and how their responsibilities, technical depth, and candidate pools differ
• Can confidently discuss LLM training and fine-tuning, inference, evaluations, data pipelines, model behaviour, multimodal learning, agent systems, and ML infra
• Can interpret technical profiles beyond keywords, job titles, or company names
• Know how to assess the significance of a candidate’s publications, open-source work, models, datasets, experiments, and production systems
• Understand where exceptional AI research talent can be found and how to reach candidates who are not visible through conventional sourcing channels
• Participate in or closely follow AI research and engineering communities
• Can build credibility with highly technical candidates and communicate nuanced research problems accurately
• Have experience recruiting across the US and European markets
• Can act as a trusted talent advisor to technical hiring managers and research leadership
• Are comfortable operating with ambiguity and continuously refining profiles as research priorities evolve
• Are willing and able to recruit beyond Research roles when needed
• Speak and write fluent English (C1+)
• A big plus
• Experience recruiting for a frontier AI lab, AI safety org, research-focused startup, or leading AI company
• A strong existing network among AI researchers, research engineers, and ML infra specialists
• Experience sourcing through publications, conference programs, citations, GitHub, open-source projects, research communities, and academic labs
• A technical or research background in computer science, machine learning, mathematics, or a related field
• Experience recruiting for an early-stage startup where the research direction and hiring requirements evolve quickly