AI Safety & Data Quality Analyst (HITL) - California

Welo Data; California (MO); On-site; USD 36,368 - 51,797; Full time

Benefits offered by this job

Gourmet dining

Endless snacks

Access to rooftop nature parks

Free transport and shuttles

Comprehensive Medical, Dental, and Vision

15 days Paid Sick/Holiday time

Job summary

A data analytics company is seeking a Data Quality Analyst to evaluate AI outputs and ensure safety and accuracy. This role requires a university degree and at least 2 years of relevant experience. The candidate should possess strong writing skills, critical thinking, and technical curiosity. Benefits include free meals, comprehensive medical coverage, and paid time off. This position is on-site in California.

Qualifications

• 2+ years of experience focusing on Safety & Trust in GenAI data delivery.
• 1–2 years of professional writing experience.
• Proficiency in languages other than English is highly preferred.

Responsibilities

• Critique AI outputs and provide structured feedback.
• Identify bias, risks, and edge cases in models.
• Spot patterns in model failures and communicate insights.

Skills

Attention to detail

Critical thinking

Technical curiosity

Multilingual proficiency

Education

University degree (Bachelor’s or higher)

Job description

Join the Human Intelligence Behind the World’s Leading AI – California

Are you sharp, curious, and obsessed with the details? Welo Data is looking for Data Quality Analysts who can go beyond simple labels to help define how AI should behave in the real world.

This is a Human-in-the-Loop (HITL) role where your judgment is the product. You won't just be following guidelines—you’ll be refining them, identifying failure modes, and ensuring that the next generation of AI is safe, accurate, and helpful.

The Mission: Beyond Annotation

• Model Evaluation: Critique AI outputs and provide structured, high-level feedback that drives model improvement.
• Safety Engineering: Use your 2+ years of GenAI safety experience to identify bias, risks, and edge cases.
• Trend Analysis: Spot patterns in how models fail and communicate these insights to stakeholders.
• Framework Architecture: Don’t just execute workflows—help improve the evaluation frameworks themselves.
• Collaborative Calibration: Partner with leads and cross-functional teams to set the gold standard for AI quality.

Project Details

• Job Title: Data Quality Analyst
• Employment Type: W2 Full-Time Employee
• Hours: 40 hour/week
• Location: California (On-site) — San Francisco, Sunnyvale, or Burlingame
• Pay Rate: 32$/hour

Benefits

Working on-site at our California office comes with premium benefits designed to let you focus on your best work:

• Gourmet Dining: Free breakfast, lunch, and dinner with a wide variety of cuisines.
• Endless Snacks: Micro-kitchens stocked with premium coffee, beverages, and healthy treats.
• Campus Life: Access to unique features like rooftop nature parks and modern collaborative spaces.
• Commuter Ease: Free transport, shuttles, and bike-to-work perks.
• Full Security: Comprehensive Medical, Dental, and Vision + 401(k) and HSA.
• Time Off: 15 days of combined Paid Sick/Holiday time + major holidays.

Who We’re Looking For

• Education & Language: University degree (Bachelor’s or higher) and Native-level English proficiency are required.
• The Multilingual Edge: Proficiency in languages other than English is highly preferred, with a particular interest in candidates demonstrating bilingual mastery in French, Italian, German, Spanish, or Brazilian Portuguese.
• Writing Mastery: 1–2 years of professional writing experience with the ability to articulate complex "why" justifications clearly.
• AI Safety Expertise: 2+ years of experience focusing on Safety & Trust in GenAI data delivery.
• Critical Thinking: The ability to navigate "grey areas" and make sound judgment calls when guidelines are ambiguous or evolving.
• Technical Curiosity: A natural interest in LLMs and the confidence to speak up and ask questions when something feels "off."
• Operational Mindset: Experience in data quality, QA, or annotation is helpful, but a strong analytical mind is essential.