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.

Tasks

What you'll 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 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.

Requirements

You're a strong candidate if you:

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

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

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, we'd also love to know which ML/AI research areas and conferences you’re most familiar with.

We look forward to hearing from you!

Originally posted on Himalayas