Remote ML/AI Research Evaluator — Top-Tier Papers

Lightly AG; Schweiz; Remote; CHF 96,000 - 152,000; Part time

Benefits offered by this job

Fully remote

Flexible hours

Part-time contract

Job summary

Lightly AG, a Zurich-based AI company, seeks experienced ML/AI researchers to evaluate advanced AI-generated research for quality and novelty. This remote, project-based contractor role emphasizes critical evaluation, methodological rigor, and evidence-based feedback aligned with leading ML/AI conferences.

Applicants should hold a PhD in ML/AI/CS or related field, have multiple peer‑reviewed NeurIPS/ICML/ICLR papers, and possess hands-on ML research experience with strong writing skills.

Qualifications

• PhD in ML/AI or closely related field (or equivalent research track).
• At least 3 peer‑reviewed papers at NeurIPS/ICML/ICLR (required).
• Hands‑on ML/AI research experience with novel methods and experiments.
• Strong understanding of scientific methodology and reproducibility.
• Experience reviewing papers for top ML/AI venues is preferred.

Responsibilities

• Critically evaluate AI-generated ML/AI research papers for contributions and methodology.
• Assess novelty, significance, and technical soundness of proposed approaches.
• Evaluate experimental design, baselines, metrics, ablations, and reproducibility.
• Identify flaws, unsupported claims, and inconsistencies in results.
• Review literature for overlooked prior work and proper citations.
• Provide structured, evidence-based feedback and scoring using rubrics.
• Ensure assessments meet high conference standards (NeurIPS/ICML/ICLR).
• Deliver clear written evaluations with reasoned judgments.

Skills

PhD in ML

Publications in NeurIPS/ICML/ICLR

Hands-on ML research

Technical writing

Education

PhD in ML/AI/CS

Job description

Lightly AG, a Zurich-based AI company, seeks experienced ML/AI researchers to evaluate advanced AI-generated research for quality and novelty. This remote, project-based contractor role emphasizes critical evaluation, methodological rigor, and evidence-based feedback aligned with leading ML/AI conferences.

Applicants should hold a PhD in ML/AI/CS or related field, have multiple peer‑reviewed NeurIPS/ICML/ICLR papers, and possess hands-on ML research experience with strong writing skills.