AI Trainer (ML Engineering)

micro1; United States; Remote; USD 80,000 - 120,000; Full time

Job summary

micro1 seeks a candidate with hands-on AI evaluation experience to train, benchmark, and improve AI systems. You will provide evidence of skill through public models, datasets, or competitions, and demonstrate practical Python expertise with popular ML libraries.

Ideal applicants showcase publicly verifiable work (GitHub, Kaggle, Hugging Face, or research papers) and can begin immediately. This role focuses on rigorous evaluation methodologies and reproducible results.

Qualifications

• Prior paid experience training, evaluating, benchmarking, or improving AI systems.
• Strong practical proficiency with Python.

Skills

Python programming

Public Proof of Skill

Tools

PyTorch

JAX

TensorFlow

scikit-learn

XGBoost

LightGBM

Job description

Required Qualifications

• Prior paid experience training, evaluating, benchmarking, or improving AI systems.
• Strong practical proficiency with Python.
• Hands-on experience using PyTorch, JAX, TensorFlow, scikit-learn, XGBoost, LightGBM, or comparable ML tools.
• Public Proof of Skill

Strong applicants should provide evidence such as:

• GitHub repositories or public model implementations
• Kaggle, DrivenData, AIcrowd, or similar competition results
• Hugging Face models or datasets
• Research publications or technical reports
• Open-source ML contributions
• Public benchmarks, experiments, or technical projects

Preferred Qualifications

• Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.
• Experience reviewing other AI trainers’ technical submissions.
• Kaggle Expert, Master, or Grandmaster status, or strong results in another technical competition.
• Publications or open-source contributions in machine learning.
• Prior experience with long-horizon or agentic AI evaluations.
• 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.