AI Trainer – Physical Sciences - Remote

Location: Global
Work Arrangement: Fully Remote
Employment Type: Independent Contractor
Time Commitment: 15+ hours per week
Schedule: Flexible
Compensation: $115,200–$230,400/year equivalent
Hourly Equivalent: $60–$120/hour, paid per accepted task

Annualized compensation is an illustrative equivalent based on 40 hours/week × 48 weeks/year. This is an output-based engagement, not a guaranteed salary. Payment is determined by accepted tasks that meet project specifications and quality standards. Actual earnings depend on task availability, completion time, and approved work.

About the Role

We are seeking experienced Computational Physical Scientists with a background in AI training, model evaluation, or technical benchmarking to help improve advanced AI systems.

In this fully remote contractor role, you will combine deep scientific expertise with practical programming skills to develop challenging computational tasks, assess AI-generated solutions, and establish rigorous standards for technical accuracy.

You will evaluate scientific calculations, code, simulations, and models, identifying errors in methodology, numerical results, assumptions, and reproducibility. This opportunity is intended for professionals who have already completed substantive AI-training or model-evaluation work involving scientific or technical content.

Key Responsibilities

• Create realistic computational physical-science tasks that challenge advanced AI models.

• Develop clear task instructions, reproducible computational environments, reference solutions, and objective evaluation criteria.

• Evaluate AI-generated calculations, programming code, simulations, scientific models, and technical deliverables.

• Identify incorrect assumptions, numerical inaccuracies, flawed methodologies, physically impossible results, and reproducibility issues.

• Assess the scientific validity and technical quality of model-generated outputs.

• Establish standards for determining whether solutions are accurate, complete, and scientifically defensible.

• Apply computational methods and domain expertise to improve AI performance on complex physical-science problems.

Required 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, such as physics, astronomy, cosmology, materials science, chemistry, computational physics, or computational chemistry.

• Hands-on experience applying computational methods to modeling, simulation, numerical analysis, data analysis, or reproducible scientific research.

• Ability to evaluate technical solutions rigorously and identify scientific, mathematical, and computational errors.

• Strong analytical reasoning and technical problem-solving skills.

Public Proof of Technical Expertise

Applicants should be prepared to provide verifiable evidence of their scientific and computational work. Relevant examples include:

• Peer-reviewed publications or preprints involving computational methods.

• Public simulation, modeling, or scientific analysis code.

• Research profiles such as Google Scholar, ORCID, or arXiv.

• Contributions to open-source scientific software.

• Reproducible notebooks, computational models, datasets, or simulations.

• Public benchmarks, experiments, or technical research projects.

Preferred Qualifications

• Experience developing AI-training tasks, reference answers, evaluation rubrics, automated graders, or benchmark problems.

• Familiarity with scientific computing and machine-learning libraries such as NumPy, SciPy, SymPy, Astropy, PyTorch, and JAX, or comparable tools.

• Experience validating simulations and computational models against analytical solutions, experimental results, or observational data.

• Availability to begin immediately.

Application and Selection Process

Candidates should submit an application outlining their previous AI-training or model-evaluation experience, primary scientific specialization, and relevant public technical evidence.

The selection process includes:

1. Application Review: Assessment of AI-training experience, scientific specialization, and technical evidence.

2. AI Interview: Technical assessment incorporating integrity checks.

3. Hiring Manager Review: Evaluation of overall suitability for the project.

4. Project Onboarding: Selected candidates complete onboarding before beginning their first assigned task.

Compensation Structure

This is an output-based contractor engagement. Experts are compensated for tasks that meet the project's specifications and quality requirements. Task availability and completion time may vary, so earnings are dependent on accepted deliverables rather than guaranteed working hours.

Professionals with demonstrated AI-training experience, strong Python skills, and verifiable computational physical-science expertise are encouraged to apply.