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Bilingual AI Response Evaluator

Job in Toronto, Ontario, C6A, Canada
Listing for: Obsidian
Full Time position
Listed on 2026-06-10
Job specializations:
  • Language/Bilingual
    Bilingual
Salary/Wage Range or Industry Benchmark: 60000 - 80000 CAD Yearly CAD 60000.00 80000.00 YEAR
Job Description & How to Apply Below

PLEASE NOTE:

You must take the Bilingual Competency interview in Russian to be considered for this role.

Location

Global

Type

Contract Work

Fluent Language Skills Required

Russian (native fluency) and English (strong proficiency)

Why this role matters

Your job is to assess Russian AI-generated responses and identify specific strengths and areas of improvement for these responses – your work will be used to create the “perfect AI-generated response” at a later stage of this project. Note the analysis you create will be in English.

What You'll Do
  • Conduct fact-checking using trusted public sources and external tools
  • Generate high-quality human evaluation data by identifying response strengths, areas for improvement, and factual inaccuracies
  • Assess reasoning quality, clarity, tone, and completeness of responses
  • Ensure model responses align with expected conversational behavior and system guidelines
Who You Are
  • You hold a Bachelor's degree
  • You are a native speaker in Russian
  • You have significant experience using large language models (LLMs) and understand how and why people use them
  • You have excellent writing skills in English and can clearly articulate nuanced feedback
  • You have strong attention to detail and consistently notice subtle issues others may overlook
  • You have a background or experience in domains requiring structured analytical thinking (e.g., research, policy, analytics, linguistics, engineering)
Nice-to-Have Specialties
  • Prior experience with RLHF, model evaluation, or data annotation work
  • Experience writing or editing high-quality written content
  • Experience comparing multiple outputs and making fine-grained qualitative judgments
What Success Looks Like
  • You identify factual inaccuracies, reasoning errors, and communication gaps in model responses
  • You produce clear, consistent, and reproducible evaluation artifacts
  • Your feedback leads to measurable improvements in response quality and user experience
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