Remote Biology Expert (PhD) - AI Trainer ($80-$90 per hour)Mercor • New York, New York, US
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Remote Biology Expert (PhD) - AI Trainer ($80-$90 per hour)
Mercor • New York, New York, US
[job_card.variable_days_ago]
[job_preview.job_type]
- [job_card.full_time]
- [filters.remote]
[job_card.job_description]Ideal Qualifications : Have a PhD in Biology from a top US university. - Previous experience in both - Have high attention to detail. Have exceptional written and verbal communication skills. Have excellent proficiency in English. - Are a citizen of U.S., Canada, New Zealand, UK, or Australia. - Are currently based in the U.S., Canada, New Zealand, UK, or Australia. Key Responsibilities : You will work asynchronously with a team of highly qualified experts across your domain. - You will craft, solve, and review challenging problems with real world applicability. - The work is fully remote and asynchronous Timeline The project will entail 16 hours of work over Friday 12 / 12 to Sunday 12 / 14 . - You must be available to work all 3 days to be considered for this project. No exceptions will be made. Interview Process You will complete a short interview and questionnaire to assess your domain expertise. - You will be paid for up to 1 hour of onboarding time including the screening process and a few onboarding videos if you are hired. Compensation and Legal Details You will be legally classified as an hourly contractor for Mercor. - You will be paid out at the end of each week via Stripe Connect. About Mercor Mercor connects elite creative and technical talent with leading AI research labs, headquartered in San Francisco, CA. Our distinguished investors include Benchmark, General Catalyst, Peter Thiel, Adam D’Angelo, Larry Summers, and Jack Dorsey.
Mercor is seeking
- Biology PhDs
- for a premier project with one of the world's top AI labs.
In this role, you will contribute your subject matter expertise to a cutting-edge project involving frontier large language models. Specifically, you will craft high-quality, challenging problems in your domain expertise with real-world applicability to inform the future of AI innovation.
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