AI Trainer (Physical Sciences)

micro1 Inc.; United States; Remote; USD 83,000 - 165,000; Part time

Job summary

micro1 Inc. seeks Computational Physical Scientists with AI-training or model-evaluation experience to develop challenging tasks that probe advanced AI systems.

You will craft realistic problems, establish evaluation criteria, and build reproducible environments to test model outputs and claims. The role emphasizes identifying errors, invalid assumptions, and reproducibility issues, requiring strong Python skills and deep domain knowledge in physics, chemistry, or related fields.

Qualifications

• Paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
• Strong practical proficiency with 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.

Responsibilities

• Create realistic computational physical-science tasks that challenge advanced AI models.
• Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
• Evaluate model-generated calculations, code, simulations, models, and technical deliverables.
• Identify invalid assumptions, numerical errors, physically impossible results, incorrect methods, or reproducibility failures.

Skills

Python

AI systems experience

Numerical analysis

Tools

NumPy

SciPy

SymPy

Astropy

PyTorch

JAX

Job description

Pay: $60–$120/hour equivalent, paid per accepted task

Location: Global, fully remote

Job Type: Contractor

Time Commitment: 15+ hours per week

Schedule: Flexible

micro1 is looking for Computational Physical Scientists with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems.

You will use your scientific expertise and programming skills to create challenging technical tasks, evaluate model-generated code and analyses, identify failures, and establish objective standards for correct solutions. This role is intended for candidates who have completed substantive AI-training work involving physical-science content.

What You’ll Work On

1. Create realistic computational physical-science tasks that challenge advanced AI models.
2. Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
3. Evaluate model-generated calculations, code, simulations, models, and technical deliverables.
4. Identify invalid assumptions, numerical errors, physically impossible results, incorrect methods, or reproducibility failures.

Required Qualifications

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

Relevant specializations may include physics, astronomy and cosmology, materials science, chemistry, computational physics, computational chemistry, and scientific simulation.

Public Proof of Skill

Strong applicants should provide evidence such as:

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

Preferred Qualifications

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

Compensation

1. Compensation is output-based. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.