Part-Time AI Research & Benchmarking Assistant

Mercor

San Francisco (CA)

On-site

USD 34,000 - 62,000

Part time

14 days+
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Job summary

Mercor is seeking detail-oriented researchers to support a cutting-edge AI benchmarking project in San Francisco. Ideal candidates have 2-3 years of experience in STEM or related fields and are currently enrolled in a Masters program or recently completed one. The role emphasizes rigorous online research and clear summaries.

Work is part-time, roughly 10-20 hours per week, with an immediate start. You will develop high-quality research questions, answers, and evaluation materials to train

Qualifications

  • All experts must have a Macbook with an M-series chip.
  • Masters student or 2-3 years experience + Bachelor's from a prestigious institution.
  • Strong online research and communication skills.
  • Ability to gather and summarize information from diverse sources.
  • Excellent written communication skills.
  • Interdisciplinary degree is a nice to have.
  • Macbook must be Apple Silicon (ARM), M-series Pro, or MacBook Air with macOS 15+.

Responsibilities

  • Part-time commitment of approximately 10-20 hours per week.
  • Create high-quality research questions, answers, and evaluation materials to train advanced language models.
  • Structured responsibilities without high-pressure internship or full-time consulting expectations.
  • Immediate start preferred.

Skills

Online research
Written communication
Summarization

Education

Master's degree
Bachelor's degree from a prestigious institution

Job description

Mercor is seeking detail-oriented researchers to support a cutting-edge AI benchmarking project in San Francisco. Ideal candidates have 2-3 years of experience in STEM or related fields and are currently enrolled in a Masters program or recently completed one. The role emphasizes rigorous online research and clear summaries.

Work is part-time, roughly 10-20 hours per week, with an immediate start. You will develop high-quality research questions, answers, and evaluation materials to train

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