How to apply for Recruiter (Research)

About White Circle

White Circle builds a safety, reliability, and optimization layer for AI systems. It has raised $70M total, processes over 100 million API calls per month, and fine-tunes its own LLMs. The team is small and focused, and work ships to production quickly.

About the role

This is a full-cycle recruiter role covering AI research, machine learning, data, evaluations, and ML infrastructure hiring. You will own hiring for roles including ML/AI Research, Multimodal ML Engineer, Research Scientist (AI Behaviours, Evals, Agentic Systems), and ML Infra. The work directly shapes the team building AI safety systems.

A typical day

A typical day might involve sourcing candidates for open research and ML infra roles, screening resumes against technical requirements, and coordinating interviews with hiring managers. You would also spend time learning about White Circle's research agenda and updating candidates on the company's work in AI safety. Exact routines depend on team size and current hiring priorities, so ask about the weekly recruiting cadence in interviews.

Who White Circle is looking for

• Has full-cycle recruiting experience for AI research, ML engineering, data, evals, or ML infrastructure roles.
• Knows where to find AI researchers and engineers, including non-traditional sources like research labs, open-source projects, and academic networks.
• Can learn and speak to technical topics such as LLM fine-tuning, evaluations, agentic systems, and multimodal ML.
• Has recruited in a small, fast-moving startup environment where roles are broad and priorities shift.

Tips for this application

• Name specific AI research communities, labs, or conferences where you have sourced candidates, such as NeurIPS, ICML, or open-source ML projects.
• List the exact AI/ML role types you have hired for, using terms from the job description like evals, agentic systems, or ML infra.
• Explain how you learn enough about a research agenda to evaluate and sell candidates on technical work.
• Include metrics that matter for this role, such as time-to-hire for senior research roles or offer acceptance rates.
• Reference White Circle's policy-based approach to AI safety in your application to show you understand the product.

What to cover in your cover letter

['Your experience sourcing and closing AI researchers or ML engineers, with examples of specific roles or labs.', 'How you build a pipeline in a competitive market where candidates are not actively looking.', 'Your ability to understand and communicate technical research topics to candidates.', 'Why you want to work at a small AI safety company that ships to production quickly.']

Research before applying

• Read White Circle's website to understand what policies are and how the platform tests and enforces them.
• Look up the investors listed, including people at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, and Sentry, to understand the company's network.
• Find and read any public writing or talks from White Circle about fine-tuning their own LLMs or processing 100 million API calls per month.
• Check the backgrounds of current team members to understand what kind of researchers and engineers they hire.

Likely interview topics

Based on the job description, expect questions about:

• How you would source and engage passive AI researchers for roles in evals, agentic systems, or multimodal ML.
• Your process for managing full-cycle hiring across multiple technical roles at once in a small team.
• How you assess whether a candidate has the right research depth for a role like Research Scientist (AI Behaviours).
• What you know about the AI safety talent market and where White Circle can compete for candidates.
• How you would explain White Circle's policy-based safety layer to a skeptical senior researcher.

Common mistakes to avoid

• Sending a generic recruiting cover letter that does not mention AI research, evals, or ML infrastructure roles.
• Claiming AI/ML hiring experience without naming specific roles, labs, or candidate profiles.
• Focusing only on process or tools instead of showing you can find and close technical candidates in a competitive market.