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 usWhite 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 raised $11M from 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 AI Research, ML, data, evaluations, and ML infra roles
Develop a deep understanding of White Circle’s research agenda, technical challenges, and the profiles required to solve them
Partner closely with research leadership and CEO to define highly specialised candidate profiles
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
Follow developments in AI safety, model behavior, evaluations, multimodal systems, agentic systems, data quality, post-training, and ML infrastructure
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
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 behavior, 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
Meaningful equity package
Paid time off in line with your local regulations, no matter where you work from
All the hardware, tools, and services you need
Covered subscriptions for AI agents
Team off-sites twice a year: we’ve recently been to the Alps and to Saint-Tropez
Introductory call with Head of People (25 min)
Take-home test
Final conversation with our CEO & Research Lead (45 min)
Please submit your application in English.
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