Remote Bayesian Statistics & AI Problem Designer

Mercor; San Francisco (CA); On-site; USD 96,432 - 137,760; Part time

Job summary

Mercor is seeking a Computational Bayesian Statistics and Applied Mathematics Expert to tackle challenging computational problems for AI applications. This remote position requires a commitment of 15-20 hours per week, focusing on designing tests to evaluate AI capabilities in scientific tasks.

The ideal candidate will hold graduate-level STEM training and possess strong Python skills, with familiarity in scientific software libraries. The role promotes independent work and offers challenge-focused collaborations.

Qualifications

• Graduate-level training in a relevant STEM field.
• Proven proficiency with a scientific software library.
• Strong Python skills for writing problem setups.

Responsibilities

• Design computational problems to test AI capabilities.
• Develop problems needing specialized scientific software.
• Refine problems through testing against AI models.

Skills

Proficiency in scientific software libraries (e.g., PyMC, FEniCS)

Strong Python skills

Independent working capability

Comfortable in Linux/terminal

Education

Graduate-level training in a relevant STEM field (MS, PhD)

Job description

Mercor is seeking a Computational Bayesian Statistics and Applied Mathematics Expert to tackle challenging computational problems for AI applications. This remote position requires a commitment of 15-20 hours per week, focusing on designing tests to evaluate AI capabilities in scientific tasks.

The ideal candidate will hold graduate-level STEM training and possess strong Python skills, with familiarity in scientific software libraries. The role promotes independent work and offers challenge-focused collaborations.