AI Research Peer Review Evaluator – ML/AI

🔥 3 hours ago

🇨🇭 Switzerland – Remote

💵 $30 - $50 / hour

⏱ Part Time

🟡 Mid-level

🟠 Senior

🧠 AI Research Scientist

👻 Ghost score 7%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Lightly

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2019

🤖 Artificial Intelligence

🤝 B2B

☁️ SaaS

Artificial Intelligence • B2B • SaaS

Lightly is a company that specializes in optimizing machine learning workflows by selecting the most valuable data for training AI models, thereby reducing redundancy and bias. Their innovative toolkit supports advanced vision-language and generative AI development, enabling enterprises, researchers, and startups to manage their data and machine learning pipelines more efficiently. Through solutions like Lightly One, Edge, and Train, they help customers significantly improve model performance while minimizing data labeling costs.

📋 Description

• Read and scan ML/AI research papers to understand their core contributions, methodology, experiments, and claims • Review original human peer reviews to establish an expert baseline for each paper • Evaluate AI-generated peer reviews against that baseline using a structured scoring rubric • Assess the technical accuracy, analytical depth, constructive value, and novelty/significance assessment of each AI review • Identify hallucinations, unsupported claims, missed technical issues, or valuable insights surfaced by AI reviewers • Compare two AI-generated reviews side-by-side and determine where one provides stronger or more useful analysis • Search and verify relevant academic literature using Google Scholar, arXiv, or Semantic Scholar • Check whether cited prior work was available before the paper’s submission date • Provide concise, evidence-based rationales explaining evaluation decisions and consistently apply the project rubric • Evaluate whether agentic AI reviewers provide meaningful value beyond expert human reviewers

🎯 Requirements

• Have a Master’s, PhD, or are currently pursuing graduate study in Machine Learning, Artificial Intelligence, Computer Science, Statistics, or a closely related technical field • Have contributed to at least one scientific/research paper, ideally as a first author, although co-authors and other substantial contributors are also welcome • Have experience critically reading ML/AI research papers, including evaluating methodology, experimental design, results, limitations, and scientific claims • Be familiar with major ML/AI research venues, such as NeurIPS, ICML, ICLR, ACL, CVPR, or comparable conferences and journals • Have prior academic peer-review experience, ideally for an ML/AI conference or journal — strongly preferred • Be comfortable conducting academic literature searches and verifying prior work, publication dates, citations, and novelty claims • Have strong analytical and written communication skills and can distinguish meaningful technical concerns from superficial criticism • Can provide clear, concise, evidence-based rationales for your decisions • Can consistently apply detailed evaluation guidelines and scoring rubrics across multiple papers and reviews • Have strong attention to detail, particularly when identifying factual inaccuracies or hallucinated technical claims

🏖️ Benefits

• Fully remote and flexible — work from anywhere • Part-time contractor role with flexible hours • Work directly on the evaluation of cutting-edge agentic AI systems for scientific research • Apply your ML/AI research expertise to help measure and improve the quality of AI-generated scientific peer review