Arabic Data Labeling Associate - WA based

Welo Data; Redmond (WA); On-site; USD 70,000 - 100,000; Full time

Benefits offered by this job

Free breakfast, lunch, and dinner

Snack suites stocked with premium treats

Comprehensive Medical, Dental, and Vision

401(k) and HSA eligibility

Free transport and bike-to-work perks

Job summary

Welo Data is seeking a Data Labeling Associate for Project Perseus in Redmond, Washington. The ideal candidate has professional-level proficiency in Arabic, along with 2+ years of experience in Generative AI safety and 1–2 years in professional writing. This role involves evaluating AI systems for accuracy and bias while collaborating with technical teams. The position offers full-time employment with generous campus benefits and a competitive pay rate of $34/hour.

Qualifications

• 2+ years of experience in GenAI safety roles required.
• 1–2 years of experience in journalism or content creation is essential.
• Ability to make decisions in ambiguous scenarios is necessary.

Responsibilities

• Critique Arabic AI outputs for accuracy and fluency.
• Identify risks and bias in Arabic datasets.
• Help refine evaluation frameworks to reflect real-world dialects.

Skills

Professional-level proficiency in Arabic

B2 level or superior English skills

Experience in professional writing

AI Safety expertise

Education

Bachelor's degree or higher

Job description

Refine the Future of Multilingual AI

Do you have professional-level proficiency in Arabic and a background in writing, with attention to 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: Professional-level proficiency in Arabic and a bachelor’s degree 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

• 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.