AI Trainer (Physical Sciences)

Overview

micro1 is seeking computational physical scientists with prior professional AI-training or model-evaluation experience to help improve advanced AI systems. This contractor role is fully remote, global, and designed for candidates with substantive experience working on physical-science content in AI training contexts.

The work is output-based and paid per accepted task. Availability is flexible, with a target commitment of at least 15 hours per week.

What You’ll Work On

• 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, and reproducibility failures.

Requirements

• Prior 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 with 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

Preferred Qualifications

• Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.
• Experience 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

1. Submit your application with AI-training experience, specialization, and public technical evidence.
2. Complete an AI interview with technical and integrity checks.
3. Complete a hiring-manager review.
4. If selected, complete project onboarding and begin your first assigned task.

Compensation

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

Public Proof of Skill

• Peer-reviewed publications or preprints involving computational methods.
• Public simulation or 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.