$75-90/hrAs published by the platform.
This remote, part-time contract asks researchers to write and check demanding problems that test how AI systems reason across mathematics, the sciences, and engineering. It is aimed at people with a Master's or PhD who can work independently and asynchronously with AI researchers and domain experts.
$75-90/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/00a9d0bc-d28d-48ae-8303-ced077c355b6
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 role invites PhD-level applied physicists to evaluate and improve how AI models reason about physical systems. You will design advanced physics problems, author rigorous solutions, audit AI-generated reasoning for physical consistency, and provide expert feedback to refine model behavior across classical mechanics, electrodynamics, quantum mechanics, and related domains.
This role involves evaluating AI-generated chemical reasoning and responses to identify flawed mechanisms, unsafe procedures, and analytical errors. You will write prompts to test chemical knowledge, review AI outputs for errors, and provide correct expert-level answers with proper attention to safety and experimental conditions.
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.
You will work on a research benchmark in condensed matter physics, by creating, solving, revising, or auditing advanced physics problems designed to test AI models. This role requires published expertise in a specific area of condensed matter and the ability to present your reasoning in a complete and verifiable manner for review by a peer specialist.
Posted September 25, 2026
This role offers physicists specialized in fluid dynamics the opportunity to contribute to CritPt, a research benchmark testing the physical reasoning capabilities of frontier AI models. You will create, solve, or review research problems on linear stability of buoyancy-driven convection, in Rayleigh-Bénard or porous media, depending on your publication domain.
Posted September 25, 2026
