AI Trainer (Physical Sciences) at micro1
Job details
Use AI to assess how you fit
About this role
Pay: $60–$120/hour equivalent, paid per accepted task Location: Global, fully remote Job Type: Contractor Time Commitment: 15+ hours per week Schedule: Flexible micro1 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. Originally posted on Himalayas