Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.
- Design challenging computational problems to test AI systems using real scientific software.
- Develop problems requiring strategic planning and multi-step workflows for AI to uncover hidden data.
- Refine problems through iterative testing against state-of-the-art AI models.
- Utilize astropy and related tools for cosmological calculations and data analysis.
- Work independently to enhance problem designs based on feedback.
- Collaborate asynchronously with teams to ensure problem difficulty is optimal.
Qualifications
Must-Have
- Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience).
- Proven proficiency with scientific software libraries through research publications or professional work.
- Solid Python skills for writing problem setups and solution validators.
- Comfortable in a Linux/terminal environment with remote compute sandboxes.
- Available for at least 15–20 hours per week.
Preferred
- Experience across multiple domains or tools.
- Familiarity with benchmark or evaluation design.
- Background in scientific teaching or exam/problem-set design.
- Experience with computational reproducibility and containerized environments.
Application Process (Takes 20–30 mins to complete)
- Upload resume
- AI interview based on your resume
- Submit form
Resources & Support
- For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
- For any help or support, reach out to:
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.