AI Trainer (Physical Sciences)
Part-time
micro1
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.
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Vacancy posted 1 day ago
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