$170-200/hrAs published by the platform.
This contract role asks mathematicians to turn informal proofs into machine-checkable Lean 4 code, helping AI systems learn to reason about formal mathematics. It suits researchers with strong proof-writing backgrounds who enjoy structuring arguments for proof assistants and examining where automated provers fail.
$170-200/hr
Pay as published by the platform. It is not a guarantee of income or hours.
The platform has not published which countries are eligible for this job.
You apply on Alignerr, Labelbox's expert network: create a profile, complete an AI-led interview and skills assessment, then get matched to projects in your field.
Official posting: https://www.alignerr.com/jobs/085dcd84-464a-4eeb-80a5-72458786419d
Last checked on October 8, 2026.
This role involves translating mathematical proofs into machine-verifiable Lean 4 formalizations for an AI research organization. Mathematicians with formal proof system experience will work on bridging informal mathematical arguments and automated reasoning, identifying gaps in current verification tools and helping advance mechanized mathematics at the intersection of AI and rigorous proof.
This contract role asks mathematicians to turn dense informal proofs into structured, machine-checkable Lean 4 formalizations. The work also involves helping AI researchers understand where automated provers fail and how to improve formal verification pipelines. It is aimed at people with strong proof-writing backgrounds who want to work at the intersection of mathematics and AI.
This role involves evaluating and improving how AI models handle rigorous mathematical reasoning. You will create challenging math problems, review AI solutions for logical errors and unjustified steps, and write correct solutions that serve as training examples for the models to learn from.
This role invites mathematical physicists to contribute to a research benchmark in fundamental physics, designed to assess whether cutting-edge AI models can solve real research problems in physics. You will work on specialized challenges in one of four proposed domains (special functions, integrable systems, permutation combinatorics, conformal geometry), creating, solving, reviewing, or validating problems at the level of academic research.
Posted September 25, 2026
This role involves contributing to the creation and evaluation of research-level physics challenges for a benchmark designed to test the physical reasoning capabilities of AI models. The work involves creating problems, solving them, examining solutions or auditing them in your area of specialization in statistical physics. This role is intended for PhDs in statistical physics with published experience on specific phenomena and who master the corresponding mathematical and computational methods.
Posted September 25, 2026
This ongoing project hires advanced STEM experts to write graduate-level and PhD-level problems with exact, verifiable answers, helping AI systems improve at rigorous scientific reasoning. Participants create original problems in their own specialty, write detailed step-by-step solutions, and sometimes review AI-generated answers to find reasoning errors. It is aimed at PhD candidates and holders, postdoctoral researchers, and faculty in biology, chemistry, physics, mathematics, and selected engineering fields.
Posted August 6, 2026
