Job Overview
Location
Argentina - Fully Remote
Job Type
Contract
Category
Software Engineering
Date Posted
May 21, 2026
Full Job Description
đź“‹ Description
- • Design advanced, deterministic mathematical problems that challenge state-of-the-art AI models in reasoning capabilities, ensuring each problem has exactly one verifiable correct answer.
- • Create rigorous, proof-based problems rooted in pure or applied mathematics that test deep conceptual understanding rather than rote memorization or pattern recognition.
- • Develop complete, meticulously verified solutions for every problem, including step-by-step reasoning that is logically sound, reproducible, and technically accurate.
- • Structure problems to mirror authentic mathematical workflows used in academic research, graduate education, or professional applications in STEM fields.
- • Ensure all problem statements and solutions are written with precision in clear, grammatically correct English to maintain consistency across global AI training datasets.
- • Collaborate with a remote team to produce high-quality training and evaluation data used directly by leading AI research laboratories to enhance model reasoning in technical domains.
- • Focus on generating problems that are original and not derived from publicly available textbooks or standard problem sets, ensuring novelty and difficulty appropriate for frontier AI systems.
- • Maintain strict adherence to technical specifications: each problem must be unambiguous, well-defined, and solvable through mathematical reasoning without requiring external computation or data lookup.
- • Document solution paths with explicit justifications for each step, including referenced theorems, definitions, or logical deductions to ensure transparency and verifiability.
- • Prioritize depth over breadth—problems should require synthesis of multiple mathematical concepts, abstract thinking, or non-trivial manipulations of formulas, structures, or proofs.
- • Ensure all outputs are free of typographical, notational, or logical errors, reflecting the highest standards of mathematical communication.
- • Work independently within a part-time, remote contract structure, delivering consistently high-quality problem sets on a regular basis.
- • Adapt problem complexity to target advanced AI systems currently capable of solving undergraduate to graduate-level mathematical tasks.
- • Contribute to iterative feedback cycles by refining problem designs based on model performance data and team evaluations.
- • Maintain confidentiality of all materials, as problems may be used in proprietary AI training pipelines and evaluation benchmarks.
- • Meet weekly output targets with a commitment of 10 hours per week, balancing quality with efficiency in problem creation and documentation.
- • Engage with the latest developments in AI reasoning benchmarks and mathematical evaluation frameworks to ensure problem designs remain aligned with industry standards.
🎯 Requirements
- • PhD Students or Graduated PhDs in Mathematics or a closely related field
- • Strong research or industry experience involving mathematical modeling, proof-based reasoning, applied mathematics, or computational mathematics
- • Ability to design original, difficult problems that mirror real mathematical workflows
- • Excellent attention to detail and technical writing skills in English
🏖️ Benefits
- • Compensation of up to 40 USD / hr
- • Fully remote work with global flexibility
- • Immediate start available
- • Part-time commitment of 10 hours per week
Skills & Technologies
About Anyone AI Inc.
Anyone AI provides no-code computer-vision infrastructure that lets companies build, train and deploy image and video models without machine-learning teams. Its cloud platform automates data labeling, model optimization and edge deployment for retail, manufacturing, logistics and healthcare use cases. The company offers pre-trained APIs for object detection, counting and anomaly identification, plus dashboards for continuous monitoring and compliance. Founded in 2021, it targets enterprises seeking rapid AI adoption while avoiding costly specialists and infrastructure setup.
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