AI Research Peer Review Evaluator (MLAI)

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.

Were looking for researchers with strong Machine Learning / AI backgrounds to support an AI evaluation project focused on scientific peer review. Youll 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.

What youll be doing

• 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 papers 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 reviewersfor example by identifying relevant prior literature that humans missed questioning important assumptions or resolving inconsistencies using evidence.

Youre a strong candidate if you:

• Have a Masters 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

Send us your CV along with a brief note about your research background and areas of expertise. Please include any relevant publications as well as previous peer-review experience for conferences journals workshops or similar academic venues.

If applicable wed also love to know which ML/AI research areas and conferences youre most familiar with.

We look forward to hearing from you!