WFH.teamOpen app$125k-$250k
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flexible
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What this posting tells you
Remote contractor role creating and evaluating computational physical-science tasks for AI systems. Compensation is stated as $60–$120 per hour equivalent, paid per accepted task; task availability and completion time may vary, so total earnings are uncertain. Commitment is 15+ hours per week with a flexible schedule. Global location is stated, but eligible countries must be confirmed on the official posting.
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Eligible countries are not stated.
Global
Experienced specialist; requires prior professional AI-training or model-evaluat · Contractor
- Eligible hiring countries are not stated
Backend hiring on WFH.team
This role's listed pay is near the median among 200 comparable roles shown here. Category counts come from WFH.team's latest published remote job market snapshot.
Explore the remote job marketAI Trainer (Physical Sciences) at micro1
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.
Working remotely at micro1
micro1 builds human-expertise data and evaluation infrastructure for AI models and agents. It recruits core-team employees and domain experts for AI training projects.
- Headquarters
- San Francisco Bay Area, California, United States
- Founded
- 2022
Expert opportunities are remote and can be flexible. International applicants are considered, subject to country exclusions and each project's requirements. Core-team work arrangements vary by opening; check the individual posting.
Apply through the official posting. Expert applicants complete an AI interview and skills certification, followed by project selection and onboarding. Certification does not guarantee a project or immediate work.