Mathematics Expert (Masters/PhDs)Mercor • United States
Mathematics Expert (Masters / PhDs)
Mercor • United States
15 hours ago
Job type
- Full-time
- Remote
Job descriptionIdeal Qualifications : Have a Masters or PhD in Mathematics from a top US university. - Have experience coding in Python, MATLAB for projects or research. Have field-specific experience in Numerical linear Algebra, Computational Mechanics, or Computational Finance - Have high attention to detail. Have exceptional written and verbal communication skills. Have excellent proficiency in English. - 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 project is 15-20 hours of work per week with potential for increased hours and extension. - The work is fully remote and asynchronous Role Start Date This role will begin in late December with applications reviewed on a rolling basis. Interview Process You will complete a short interview 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
- Mathematics 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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