AI Model Assessment Specialist
anonymous_hn_47996843 ·
unknownRemote
Salary Range (USD)
$22 - $70
Location
Location N/A
Visa Support
Not mentioned
Funding Stage
Unknown
Job Responsibilities
- • evaluate and stress-test AI models
- • analyze AI-generated content
- • fact-check complex outputs
- • provide detailed feedback
Required Skills
Native-level English (C1/C2)Analytical RigorAutonomy
Engineering Culture & Tech Stack
autonomy
self-starters
highly organized
Raw Post
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AI Model Assessment Specialist | REMOTE | Contract | $22 - $70/hr
We are scaling up our AI training operations and looking to bring on a large number of contractors (up to 1,000 openings) to help evaluate, stress-test, and refine next-generation AI models.
The Role:
Essentially, high-level RLHF (Reinforcement Learning from Human Feedback) and model evaluation. You will analyze AI-generated content across various domains, fact-check complex outputs, catch subtle hallucinations, and provide detailed, constructive feedback to improve model reasoning and accuracy. You'll also simulate real-world scenarios to gauge the model's actual utility.
What we are looking for:
Native-level English (C1/C2) is a hard requirement.
Analytical Rigor: High-level reading comprehension. You need to be able to spot logical flaws, edge cases, and subtle anomalies in text.
Autonomy: Self-starters who are highly organized and can reliably manage their time in a remote setup.
Bonus: Background in research, editorial work, data annotation, or tech evaluation. We index highly on people with "good taste" and refined judgment for high-quality content.
Note: The wide hourly rate ($22 - $70/hr) reflects the variance in domain expertise, assessment complexity, and location.
How to Apply:
Apply here: https://l1nq.com/ai-model-assessment-specialist
Important Next Step: Please check your inbox/spam folder immediately after submitting your application. You will receive an email containing a link to an AI-driven interview. Completing this AI interview is a mandatory requirement to move your application forward.
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Content parsed by LLM from Hacker News raw data. Confidence:HIGH