Italian Data Labeling Associate - WA

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 work streams.

• Employment Type: W2 Full-Time Employee
• Hours: 40 hours/week
• Location: Washington State (On-Site: Redmond, Seattle, or Bellevue)
• Pay Rate: 34 $ (per hour)

• Linguistic Mastery:
  Professional-level proficiency in Italian 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."

• 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.

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.