# Senior Data Labeler at Whitecircle

- Company: Whitecircle (whitecircle.com)
- Location: Paris (remote)
- Places: Paris, Remote, France
- Posted: 2026-07-08
- Apply: https://jobs.ashbyhq.com/whitecircle/0bc04858-9c98-4948-a815-9ea3debce3c8
- Page: https://ye.saranatour.dev/jobs/senior-data-labeler-whitecircle-kn7aexk2g7qmkj6yh1czaktqxh8hd620

## The posting

Senior Data Labeler
Whitecircle · Paris

**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](https://whitecircle.ai/) 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, cog