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Computational Bayesian Statistics and Applied Mathematics Expert
About the Project
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.
What You'll Do
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 — working with libraries such as:
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
Nice to Have
Please note: This application includes a coding assessment as part of the evaluation process.
Apply directly on Mercor to get started.
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