TL;DR: We're looking for a Data Labeling Lead to build our annotation team from zero – setting quality standards, designing evaluation frameworks, and running the labeling operations behind our safety evals, RLHF, and benchmarks. You'll own quality, throughput, and cost while working side by side with our AI researchers.
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 100M+ 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 at a multi-million dollar run rate already having signed customers like Lovable and multiple neobanks. You’ll be joining at the most exciting time - early enough that the equity can be life-changing, but at a point where demand has been proven.
In this role, you will
Build from scratch and lead the Data Labeling team (hiring, coaching, and performance management)
Define annotation guidelines, quality standards, and evaluation frameworks
Develop quality assurance processes, calibration sessions, and auditing systems
Partner with AI researchers and engineers to translate research objectives into labeling workflows
Prioritise labeling projects based on business and research needs
Monitor operational metrics including quality, consistency, throughput, and cost
Improve annotation tooling, automation, and workflow efficiency
Lead complex AI evaluation projects, including safety, preference ranking, RLHF, policy evaluation, and benchmark creation
Analyse disagreement patterns and edge cases to improve guidelines and model performance
Manage vendor relationships and ensure consistent quality across distributed teams
Build reporting dashboards and communicate operational insights to leadership
Foster a culture of continuous improvement, accountability, and operational excellence
We're looking for someone who
Has experience leading data annotation or AI evaluation teams
Has strong operational and people management skills
Understands AI model evaluation, LLM behaviour, and modern annotation workflows
Can design scalable processes without sacrificing quality
Communicates clearly across technical and non-technical teams
Thrives in fast-moving startup environments
You might be a great fit if you
Have managed annotation programs for LLMs, generative AI, or machine learning
Have experience with RLHF, preference data collection, safety evaluations, or benchmark creation
Have worked in Trust & Safety, AI Safety, Content Moderation, or ML Ops
Have managed distributed or global annotation teams
Have experience with vendor management and outsourcing operations
Bonus points
Familiarity with prompt engineering and AI safety policies
SQL, Python, or data analysis experience
Experience building internal annotation platforms or workflow automation
Background in linguistics, cognitive science, machine learning, or data operations
Important note
This role involves overseeing projects that may include offensive, harmful, violent, sexual, or otherwise disturbing content.
You'll be responsible for ensuring reviewers have the tools, guidance, and support necessary to perform this work safely and consistently.
Compensation & benefits
Competitive compensation, including equity
Flexible time off
A spacious office in the heart of Paris’s 2nd arrondissement, with a flexible hybrid setup
Relocation support if you’re moving to Paris, available after your probationary period
Premium private health insurance
Mental health support, including coverage for therapy when you need it
Language lessons to help you improve your English or French