AI Trainer (Physical Sciences)

micro1; United States; Remote; USD 90,000 - 140,000; Full time

Job summary

micro1 is seeking a highly skilled AI systems specialist to advance benchmarking and reproducible research in physics- and chemistry-oriented tasks. The role emphasizes Python proficiency, deep domain expertise, and hands-on experience with computational modeling and data analysis.

Candidates should provide public proof of skill such as publications, open-source code, reproducible notebooks, or benchmark results.

Qualifications

• Experience training, evaluating, benchmarking or improving AI systems on scientific tasks.
• Strong Python proficiency.
• Deep expertise in at least one physical-science specialization.
• Hands-on experience with computational methods for modeling, simulation, or data analysis.
• Evidence-based approaches and reproducible research practices.
• Public proof of skill (publications, code, notebooks).

Skills

Python

Data analysis

Physical science domain

Computational modeling

Tools

NumPy

SciPy

SymPy

Astropy

PyTorch

JAX

Job description

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.

Compensation

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