German 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 gourmet dining

Snack suites with premium offerings

Comprehensive medical, dental, and vision

401(k) and HSA eligibility

Commuter support services

Job summary

Welo Data is looking for a Data Labeling Associate to join Project Perseus in New York. This role requires native German proficiency and professional writing skills as you evaluate AI systems and improve their handling of Arabic nuances and safety. You will provide structured feedback on AI outputs, apply your expertise in AI safety, and actively participate in ensuring quality across projects. The position offers a full-time W2 position with benefits and a collaborative work environment.

Qualifications

• 2+ years of experience in AI safety focusing on Generative AI data delivery.
• 1–2 years of experience in professional writing, journalism, or content creation.
• Ability to evaluate AI outputs for accuracy and fluency.

Responsibilities

• Critique Arabic AI outputs for accuracy, fluency, and tone.
• Identify bias, risks, and failure modes in Arabic datasets.
• Participate in team syncs to ensure consistent quality across AI workstreams.

Skills

Native-level German proficiency

Professional writing experience

AI safety expertise

Critical thinking

B2 English proficiency

Education

Bachelor's degree or higher

Job description

Refine the Future of Multilingual AI

Are you a native German 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
• Pay Rate: 34 $ per hour

What We’re Looking For

• Linguistic Mastery: Native‑level German 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.