AI Trainer (Physical Sciences)

Frontier AI Lab; United States; A distancia; USD 83.000 - 165.000; A tiempo parcial

Descripción de la vacante

Remotebridge seeks Computational Physical Scientists with substantive AI-training or model-evaluation experience to advance AI systems. You will design challenging computational tasks for physics- and chemistry-focused models and evaluate their outputs with rigorous criteria.

Responsibilities include creating reproducible environments, reference solutions, and clear rubrics; identifying errors or invalid assumptions in model results.

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Python

Physical science

AI training experience

Herramientas

PyTorch

NumPy

SciPy

JAX

Astropy

Descripción del empleo

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.

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, or reproducibility failures.

Required Qualifications

• 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.

Public Proof of Skill

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

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

• 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

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