Computational Bayesian Problem Designer

Obsidian; San Francisco (CA); Hybrid; USD 70,000 - 100,000; Part time

Job summary

Obsidian is looking for a Computational Bayesian Statistics and Applied Mathematics Expert in San Francisco. You will design original and challenging problems for advanced AI systems, testing their capabilities using real scientific software.

The ideal candidate has graduate-level expertise, combines strong programming skills in Python with experience in specialized scientific software libraries, and can work independently to refine problem designs.

Qualifications

• Graduate-level training in a relevant STEM field (MS, PhD, or equivalent).
• Proven proficiency with at least one scientific software library.
• Strong Python skills with experience in writing problem setups.
• Ability to work independently and refine designs based on feedback.
• Comfortable working in a Linux environment.

Responsibilities

• Design challenging computational problems for AI testing.
• Test problems against state-of-the-art AI models.
• Refine problems until they reach target difficulty.

Skills

Computational Bayesian Statistics

Applied Mathematics

Python

MCMC

Finite Element Methods

Education

Graduate-level training (MS, PhD)

Tools

PyMC

DOLFINx

scikit-fem

GUDHI

Job description

Obsidian is looking for a Computational Bayesian Statistics and Applied Mathematics Expert in San Francisco. You will design original and challenging problems for advanced AI systems, testing their capabilities using real scientific software.

The ideal candidate has graduate-level expertise, combines strong programming skills in Python with experience in specialized scientific software libraries, and can work independently to refine problem designs.