About the job
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.
Position: Computational Bayesian Statistics and Applied Mathematics Expert Type: Contract Compensation: $70–$100/hour Location: Remote Commitment: 15–20 hours/week
Role Responsibilities
- Design challenging computational problems to test AI capabilities in solving scientific and engineering tasks.
- Develop problems requiring the use of specialized scientific software for simulations and experiment design.
- Refine problems through iterative testing against state-of-the-art AI models to achieve target difficulty.
- Collaborate on creating tasks that require strategic thinking and efficient information extraction.
- Work independently to enhance problem designs based on feedback and model performance.
Qualifications
Must-Have
- Graduate-leveltraining in a relevant STEM field ( MS, PhD, or equivalent research experience).
- Proven proficiency with at least one scientific software library ( PyMC, FEniCS, etc.).
- Strong Pythonskills for writing problem setups and solution validators.
- Ability to work independently and refine problem designs.
- Comfortable in a Linux/terminalenvironment with remotecompute sandboxes.
- Available for at least 15–20 hours/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:
- 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.
Originally posted on Himalayas
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