AI Trainer (Mathematics & Statistics) - Remote

Job Description: Computational Physical Scientist – AI Training & Model Evaluation
Role Type: Contractor
Location: Global (Fully Remote)
Time Commitment: 15+ hours per week
Schedule: Flexible
About the Role
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.

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.

- 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
Strong applicants should be able to demonstrate their expertise through evidence such as:

- 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

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Application Submission: Submit your application along with details of your AI-training experience, scientific specialization, and public technical evidence.

-
AI Interview: Complete an AI interview that includes technical and integrity checks.

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Hiring Manager Review: The team evaluates your profile and interview performance.

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Onboarding: 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.
Originally posted on Himalayas