Turkish Data Labeling Associate - NYC based

Welo Data; New York (NY); On-site; USD 70,000 - 80,000; Full time

Benefits offered by this job

Free breakfast, lunch, and dinner

Micro-kitchens stocked with beverages

Modern collaborative spaces

Comprehensive Medical, Dental, and Vision

Free transport and shuttle services

Job summary

Welo Data in New York City seeks a Data Labeling Associate for Project Perseus. This role requires professional-level Turkish proficiency and a background in writing, along with AI safety expertise. Responsibilities include evaluating AI's handling of Arabic nuances and providing feedback to improve model accuracy. Join a collaborative environment with gourmet dining, comprehensive benefits, and a focus on innovative AI solutions. Ideal candidates will have a university degree and 1–2 years of relevant experience.

Qualifications

• Professional-level proficiency in Turkish and 2+ years of GenAI safety experience.
• B2 level or superior English skills.
• 1–2 years of experience in professional writing or journalism.

Responsibilities

• Critique Arabic AI outputs for accuracy and tone.
• Identify bias and risks in Arabic datasets.
• Provide feedback to improve model performance.

Skills

Linguistic Mastery in Turkish

Professional Writing

Critical Thinking

AI Safety Expertise

Technical Agility

Education

University Degree (Bachelor’s or higher)

Job description

Refine the Future of Multilingual AI

Do you have professional-level proficiency in Turkish 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: New York City
• Pay Rate: 34 $ (per hour)

What We’re Looking For:

• Linguistic Mastery: Native-level Turkish 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.