French Data Labeling Associate - NYC based

Welo Data; New York (NY); On-site; USD 63,000 - 77,000; Full time

Benefits offered by this job

Gourmet dining

Free transport and shuttles

Comprehensive medical, dental, and vision

401(k) and HSA eligibility

Job summary

Welo Data in New York City is seeking Data Labeling Associates for Project Perseus. This full-time onsite role focuses on evaluating AI systems through the lens of Arabic language nuances. Ideal candidates will have native French proficiency, a university degree, and expertise in AI safety. The position offers a vibrant work environment with gourmet dining and comprehensive benefits, providing a unique opportunity to influence the future of AI evaluation and safety.

Qualifications

• Professional writing experience of 1-2 years, preferably in journalism or content creation.
• 2+ years of GenAI safety experience focused on identifying bias and risks.
• Ability to critique AI outputs for accuracy, fluency, and tone.

Responsibilities

• Critique Arabic AI outputs, providing structured feedback for model improvement.
• Identify risks in Arabic datasets, ensuring safety and quality.
• Communicate insights to technical stakeholders regarding trends.

Skills

Native-level French proficiency

B2 English proficiency

Professional writing experience

AI safety expertise

Critical thinking

Technical agility regarding LLMs

Education

University Degree (Bachelor's or higher)

Job description

Refine the Future of Multilingual AI

Are you a native French speaker with a professional background in writing and a keen eye for AI safety? Welo Data is looking for Data Labeling Associates for Project Perseus.

This is not a traditional annotation role. You will be working at the intersection of human judgment and machine learning, evaluating how cutting‑edge AI systems handle Arabic language nuances, cultural context, and safety boundaries.

The Mission: Human-in-the-Loop Excellence

• Model Evaluation: Critique Arabic AI outputs for accuracy, fluency, and tone, providing structured feedback to drive model improvement.
• Safety Engineering: Use your 2+ years of GenAI safety experience to identify bias, risks, and failure modes in Arabic datasets.
• Guideline Evolution: Apply complex linguistic guidelines thoughtfully and help refine evaluation frameworks to reflect real‑world Arabic dialects and contexts.
• Strategic Insights: Identify trends in model performance and communicate findings clearly to technical stakeholders.
• Collaborative Calibration: Participate in team syncs to ensure consistent quality across global AI workstreams.

Project Details

• Employment Type: W2 Full-Time Employee
• Hours: 40 hours/week
• Location: New York City On‑Site
• Pay Rate: 34 $ (per hour)

What We’re Looking For

• Linguistic Mastery: Native‑level French proficiency and a University Degree (Bachelor’s or higher).
• English Proficiency: B2 level or superior English skills to navigate technical guidelines and stakeholder syncs.
• Professional Writing: 1–2 years of experience in professional writing, journalism, or content creation.
• AI Safety Expertise: 2+ years of experience focusing on Safety & Trust in Generative AI data delivery.
• Critical Thinking: The ability to make sound judgment calls in ambiguous “grey area” scenarios.
• Technical Agility: A natural curiosity about LLMs and the confidence to flag issues when a model’s behavior feels “off.”

Premium Campus Benefits

Joining Project Perseus on‑site gives you access to a world‑class work environment:

• Gourmet Dining: Free breakfast, lunch, and dinner with a wide variety of cuisines.
• Snack Suites: Micro‑kitchens stocked with premium coffee, beverages, and healthy treats.
• Campus Features: Modern collaborative spaces and unique amenities like rooftop nature parks (at select locations).
• Full Security: Comprehensive Medical, Dental, and Vision + 401(k) and HSA eligibility.
• Commuter Support: Free transport, shuttles, and bike‑to‑work perks.

Why This Role

This is an opportunity to move beyond traditional data work and play a direct role in how AI systems are evaluated and improved. The work is fast‑moving, collaborative, and increasingly central to how modern AI systems are built and deployed.