$30-55/hrAs published by the platform.
This remote freelance role asks soil and water specialists to check AI-generated scenarios on erosion control, watershed management, and conservation planning. Reviewers flag flawed assumptions and outdated methods, then write structured feedback so the AI's answers become more accurate and practical. It is aimed at scientists with several years of hands-on experience who can work independently on task-based assignments.
$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/0a7b559f-131f-45d7-955d-8b9f2c09349e
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.
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 quantum physics problems for a benchmark designed to test the reasoning capabilities of state-of-the-art AI models. Depending on your area of specialization and publication record, you may create problems, solve them, review the work of others, or audit it. This work requires solid training in quantum information physics and verifiable publication in one of the twelve specified research domains.
Posted September 25, 2026
