$30-55/hrAs published by the platform.
This remote, task-based role asks experienced forestry and land management specialists to check how AI systems describe forest health, land use, and sustainable practices. The work involves reviewing scenarios and AI-generated answers, then pointing out factual errors, oversimplifications, and questionable recommendations. It suits professionals with several years of field experience who can work independently on their own schedule.
$30-55/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/0730835e-de0d-406c-921c-b809102432b4
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.
A drug discovery scientist will design realistic preclinical tasks and evaluate how well frontier AI models perform them, grading responses against professional standards. The role involves creating scenarios based on real workflows, running them through AI systems, and providing detailed feedback on whether the models correctly conduct SAR analysis, DMPK interpretation, screening triage, and other core discovery work.
You will work on research physics problems in optics, atomic physics or molecular physics, by creating, solving, reviewing or auditing challenges designed to evaluate the reasoning capabilities of frontier AI models. This role is for physicists who have published on specific phenomena in one of seven areas of specialization and are capable of producing research-quality work with detailed written reasoning.
Posted September 25, 2026
This role involves working on fundamental physics problems at research level for the CritPt benchmark, which evaluates the reasoning capabilities of AI models in advanced theoretical physics. Based on your profile and publications, you will create problems, solve them, review work or verify it in your area of specialization. This role is intended for high energy physicists who have published in one of seven specified domains and who master the relevant methods.
Posted September 25, 2026
