Senior Data Labeler

Visa Hunt; Paris; Hybride; EUR 65 000 - 90 000; Plein temps

Avantages offerts par ce poste

Competitive salary + equity

Hybrid work from Paris with relocation

London option (limited relocation/ins)

Paid time off

Hardware, tools & subscriptions

Team off-sites twice a year

Résumé du poste

White Circle is seeking a Data Labeling Lead to build and guide a scalable labeling program within a safety-focused AI environment. You will hire and coach a distributed team, set guidelines, and drive QA, calibration, and auditing processes in partnership with AI researchers and engineers.

You will manage vendor relationships, monitor quality and cost, and push for tooling enhancements. This role requires strong people skills and a startup mindset, with hybrid work options from Paris or London.

Qualifications

• Experience leading data annotation or AI evaluation teams.
• Strong operational and people management skills.
• Understanding AI model evaluation, LLM behavior, and modern annotation workflows.
• Ability to design scalable processes without sacrificing quality.
• Clear communication across technical and non-technical teams.
• Thrives in fast-moving startup environments.

Responsabilités

• 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.
• Prioritize 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.

Connaissances

Data labeling

Team leadership

Operational management

Communication

AI evaluation

Vendor management

Outils

Python

Description du poste

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 raised $11M from 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 behavior, 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.

Why White Circle

• Competitive salary + equity
• Work from Paris (hybrid) with a relocation package available, or work from London (note: we are currently unable to provide relocation support and medical insurance for London-based roles)
• Paid time off in line with your local regulations
• All the hardware, tools, and services you need
• Covered subscriptions for AI agents and IDEs
• Team off‑sites twice a year: we’ve recently been to the Alps and Saint‑Tropez

How we hire

1. Intro call with HR (30 min)
2. Take-home exercise
3. Final conversation with our CEO (45 min)

Please submit your application in English.