French Data Labeling Associate - WA based

Welo Data; Redmond (WA); On-site; USD 38,641 - 55,035; Full time

Benefits offered by this job

Free breakfast, lunch, and dinner

Micro-kitchens with premium snacks

Modern collaborative spaces

Comprehensive Medical, Dental, and Vision

Commuter support

Job summary

Welo Data is seeking Data Labeling Associates for Project Perseus in Redmond, WA. This role requires native-level proficiency in French and involves evaluating AI outputs for Arabic language nuances. The successful candidate will have expertise in AI safety, a background in professional writing, and a passion for ensuring high-quality AI interactions. Join a collaborative team focused on improving generative AI systems with comprehensive benefits including gourmet dining and medical coverage.

Qualifications

• 2+ years of experience focusing on AI Safety & Trust.
• 1–2 years of professional writing, journalism, or content creation experience.

Responsibilities

• Critique Arabic AI outputs for accuracy, fluency, and tone.
• Identify bias, risks, and failure modes in Arabic datasets.
• Apply linguistic guidelines to refine evaluation frameworks.
• Identify trends in model performance for communication.
• Participate in team syncs for quality assurance.

Skills

Native French proficiency

B2 English proficiency

Professional writing experience

AI safety expertise

Critical thinking

Education

Bachelor's degree 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: Washington State On-Site (Redmond, Seattle, or Bellevue)
• 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.