Bayesian Statistics Expert - Problem Designer

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-level training in a relevant STEM field ( MS, PhD , or equivalent research experience). Proven proficiency with at least one scientific software library ( PyMC , FEniCS , etc.).

Strong Python skills for writing problem setups and solution validators. Ability to work independently and refine problem designs. Comfortable in a Linux/terminal environment with remote compute 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: https://talent.docs.mercor.com/welcome For any help or support, reach out to: [email protected] PS: Our team reviews applications daily.

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