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. We’re looking for researchers with strong Machine Learning / AI backgrounds to support an AI evaluation project focused on scientific peer review. You’ll evaluate reviews generated by agentic AI systems and compare them against expert human peer reviews of ML/AI research papers.

This is a remote, project-based contractor opportunity with flexible working hours.

• 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

• 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

• 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

Originally posted on Himalayas