[Hiring] AI Trainer @YO AI Labs

Role Description

We are seeking experienced Computational Physical Scientists with prior professional AI-training or model-evaluation experience to contribute to a project focused on improving advanced AI systems. You will apply your scientific expertise and programming skills to develop challenging technical tasks, evaluate AI-generated code and analyses, identify errors, and establish objective standards for accurate solutions. This opportunity is intended for professionals with substantive AI-training experience involving physical-science content.

Key Responsibilities

• Create realistic computational physical-science tasks that challenge advanced AI models.
• Develop clear task instructions, reproducible environments, reference solutions, and evaluation criteria.
• Evaluate AI-generated calculations, code, simulations, models, and technical deliverables.
• Identify invalid assumptions, numerical errors, physically impossible results, incorrect methodologies, and reproducibility failures.
• Assess the scientific accuracy, reliability, and completeness of model-generated outputs.
• Establish objective standards for evaluating computational and scientific solutions.

Qualifications

• Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
• Strong practical proficiency in Python.
• Deep expertise in at least one relevant physical-science specialization.
• Hands-on experience using computational methods for modeling, simulation, numerical analysis, data analysis, or reproducible research.

Relevant Specializations

• Physics
• Astronomy and Cosmology
• Materials Science
• Chemistry
• Computational Physics
• Computational Chemistry
• Scientific Simulation

Public Proof of Technical Expertise

• Peer-reviewed publications or preprints involving computational methods.
• Public simulation or scientific analysis code.
• Google Scholar, ORCID, arXiv, or equivalent research profiles.
• Open-source scientific software contributions.
• Reproducible notebooks, models, datasets, or simulations.
• Public benchmarks, experiments, or technical projects.

Preferred Qualifications

• Experience creating AI-training tasks, reference answers, evaluation rubrics, graders, or benchmark problems.
• Familiarity with NumPy, SciPy, SymPy, Astropy, PyTorch, JAX, or comparable scientific tools.
• Experience validating simulations or models against analytical results, observations, or experimental data.
• Availability to begin immediately.

Application Process

• Submit your application along with details of your AI-training experience, scientific specialization, and public technical evidence.
• Complete an AI interview that includes technical and integrity checks.
• The team evaluates your profile and interview performance.
• Selected candidates complete project onboarding and begin their first assigned task.

Compensation

Compensation is output-based, with payment for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.