AI Research Peer Review Evaluator (ML/AI)

Lightly AG is a Zurich-based AI company and ETH/HSG spin-off, backed by Y Combinator and top-tier investors. Our machine learning and computer vision technology is trusted by global leaders in autonomous driving, medical imaging, and visual inspection.

• →Read and scan ML/AI research papers to understand their core contributions, methodology, experiments, and claims
• →Review the 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 the 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 sources such as Google Scholar, arXiv, or Semantic Scholar, including checking whether cited prior work was available before the paper’s submission date
• →Provide concise, evidence-based rationales explaining your evaluation decisions and consistently apply the project rubric

The evaluation specifically looks at whether agentic AI reviewers can provide meaningful value beyond expert human reviewers—for example, by identifying relevant prior literature that humans missed, questioning important assumptions, or resolving inconsistencies using evidence.

• 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
• Are 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
• Are 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