AI Research Scientist – Scientific Evaluation (ML/AI)
J
JobgetherAI research
Fully remote opportunity open to candidates worldwide.ContractMiddle
Salary50 - 65 USD per hour
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Job Details
- Required Skills
- Artificial IntelligenceMachine Learning
Requirements
- PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, or a closely related technical discipline, or equivalent demonstrated research expertise.
- At least three peer-reviewed research publications at NeurIPS, ICML, and/or ICLR.
- Extensive hands-on ML/AI research experience, including developing methods, designing experiments, analyzing results, and publishing original research.
- Strong knowledge of scientific methodology and experimental rigor, including baseline selection, statistical validity, benchmark design, ablation studies, and reproducibility.
- Ability to assess novelty, significance, technical soundness, and scientific validity independently.
- Familiarity with state-of-the-art research, established literature, and benchmarks.
- Ability to verify technical arguments, experimental evidence, scientific claims, citations, and code implementations where relevant.
- Strong analytical skills and scientific writing ability to communicate nuanced judgments through clear, evidence-based feedback.
- Ability to apply detailed evaluation guidelines and scoring rubrics consistently.
- Exceptional attention to detail when identifying methodological weaknesses, unsupported conclusions, and discrepancies between claims and evidence.
- Provide a CV, a brief overview of research background and expertise, and links to at least three qualifying peer-reviewed publications.
- First-author publications or other significant research contributions are preferred.
- Research-paper review experience for leading ML/AI conferences or journals, particularly NeurIPS, ICML, or ICLR, is strongly preferred.
Responsibilities
- Evaluate AI-generated ML/AI research papers for scientific contributions, technical correctness, methodology, experimental design, and validity of conclusions.
- Compare proposed approaches with existing research and state-of-the-art methods to assess novelty and significance.
- Examine baselines, benchmark selection, evaluation metrics, statistical evidence, and ablation studies.
- Identify methodological flaws, unsupported claims, inconsistencies, and misleading results.
- Review experimental evidence, implementation details, and accompanying code to assess reproducibility and technical soundness.
- Verify relevant literature and citations, identify overlooked prior work, and challenge unsupported novelty claims.
- Determine whether outputs meet the scientific standards of leading ML/AI conferences, including NeurIPS, ICML, and ICLR.
- Produce structured evaluations of research strengths, weaknesses, technical concerns, and scientific merit.
- Apply assessment rubrics consistently and provide clear, evidence-based justifications.
- Exercise independent expert judgment about whether AI-generated research constitutes a scientifically supported contribution.
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