AI Trainer; Physical Sciences

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

Location:

Global, fully remote

Job Type: Contractor

Time Commitment: 15+ hours per week

Schedule:

Flexible

Our team 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.

• 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.

• 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 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.

Strong applicants should provide evidence such as:

• 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

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

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

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