Applied Mathematics Specialist - Fully Remote | Upto $100/hr

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

Computational Bayesian Statistics and Applied Mathematics Expert

We're building a large-scale benchmark to test how well advanced AI systems can solve hard scientific and engineering problems. As a task designer, you'll create challenging computational problems that check whether AI can use real scientific software to do research-level work — running simulations, interpreting results, designing experiments, and uncovering hidden information from data.

This isn't a typical data-labeling job. You'll design original, graduate-level problems based on real scientific workflows, test them against cutting-edge AI models, and fine-tune them until the difficulty is just right.

You'll create problems that require skilled use of specialized scientific software. Some will ask the AI to compute exact answers from a fully defined setup — testing whether it can correctly carry out complex, multi-step workflows. Others will be harder: the AI must plan a series of queries or experiments to uncover information that isn't directly visible, which means thinking strategically about what to measure, how to read partial results, and how to narrow down the possibilities efficiently.

Each problem goes through a testing loop against state-of-the-art AI models, and you'll refine it until it hits the target difficulty.

Domains & Tools We're Hiring For

We're especially interested in experts with deep, hands-on experience in:

• Computational Bayesian Statistics and Applied Mathematics
  • Bayesian statistics: PyMC, PyStan, PyJAGS, CmdStanPy
  • Applied mathematics and numerical PDEs: FEniCS, FEniCSx, DOLFINx, scikit-fem, FiPy, Devito, Dedalus
  • Computational topology: GUDHI
  • Differential algebra: DACEyPy
  • Optimization: lmfit
• Experience with MCMC, Bayesian modeling, finite element or finite difference methods, mesh-based numerical modeling, computational topology, differential algebra, or other specialized Python-based math and statistics methods is valuable. You don't need experience with all of these — solid experience with even one will be highly regarded.
• Experience with other specialized software in this domain will also be considered.

What Makes a Strong Candidate

You have graduate-level expertise (MS or PhD preferred) in the domain above, with real hands-on experience using these tools — not just theoretical knowledge. You've written code using these libraries to solve actual research problems, and you understand where they break, what their edge cases are, and what makes a problem genuinely hard rather than just complicated.

Beyond domain expertise, the best candidates think like puzzle designers: building problems where the challenge comes from smart reasoning rather than raw computation, where several approaches seem plausible but only careful analysis reveals the right one, and where surface-level pattern matching won't get you to the answer.

Requirements

• Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience)
• Proven proficiency with at least one of the listed scientific software libraries, shown through research publications, open-source contributions, or professional work
• Strong Python skills — you'll be writing problem setups, oracle functions, and solution validators
• Ability to work independently and refine problem designs based on feedback
• Comfortable working in a Linux/terminal environment with remote compute sandboxes
• Available for at least 15–20 hours per week

Nice to Have

• Experience across multiple listed 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 Notes

This application includes a coding assessment as part of the evaluation process.