Anyone AI Inc. logo

Physics Expert - AI Trainer (UK)

Job Overview

Location

United Kingdom

Job Type

Part-time

Category

Software Engineering

Date Posted

June 21, 2026

Full Job Description

📋 Description

  • Design advanced, deterministic physics problems for training and evaluation of frontier AI models, ensuring each problem has exactly one verifiable correct answer
  • Develop complete, verified solutions with clear, step-by-step reasoning that demonstrates the full technical derivation process
  • Create problems that test deep physical reasoning, multi-step analysis, and real-world application, avoiding reliance on memorization or rote formulas
  • Utilize Python and scientific libraries (e.g., numpy, scipy) to build computational models, simulations, or workflows where relevant to problem generation
  • Ensure all problem statements, solutions, and documentation are technically precise, reproducible, and written in clear, professional English
  • Contribute to high-quality STEM training datasets used directly in AI model training pipelines at leading AI research laboratories
  • Collaborate remotely to meet project deadlines for a 2-month contract with potential for extension
  • Maintain rigorous standards for technical accuracy, logical consistency, and pedagogical depth in all submitted materials
  • Structure problems to challenge state-of-the-art AI systems by embedding non-trivial physical intuition, boundary conditions, and interdependent variables
  • Document all assumptions, constraints, and numerical methods used in problem creation to enable auditability and reproducibility by AI training teams
  • Review and refine problem sets based on feedback from internal evaluation pipelines to improve model performance in technical reasoning domains
  • Adhere to strict formatting and submission guidelines for problem and solution packages to ensure seamless integration into AI training systems
  • Work independently with minimal supervision while maintaining consistent communication and delivery timelines
  • Focus exclusively on physics domains, including but not limited to classical mechanics, electromagnetism, thermodynamics, quantum mechanics, and statistical mechanics
  • Avoid oversimplification; problems must reflect the complexity and nuance of real physics research and engineering workflows
  • Ensure all computational components, if used, are well-documented, executable, and produce deterministic outputs under specified conditions
  • Contribute to the broader mission of improving AI’s ability to reason like a trained physicist through rigorously constructed, non-trivial problems

🎯 Requirements

  • Master’s or PhD in Physics or a closely related field
  • Strong research or industry experience in theoretical, experimental, or computational physics
  • Strong Python skills with comfort using scientific libraries such as numpy, scipy, or similar
  • Solid understanding of modeling, simulation, numerical methods, and multi-step problem solving
  • Ability to design original, difficult physics problems that reflect real physics workflows
  • Excellent attention to detail and technical writing skills in English

🏖️ Benefits

  • Compensation of up to 40 USD per hour
  • Contract/part-time arrangement with flexibility to work 20 to 40 hours per week
  • Remote work opportunity based in the United Kingdom
  • Project duration of 2 months with potential for extension

Skills & Technologies

Python
NumPy
Remote
Degree Required

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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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