AI Trainer (ML Engineering)

micro1 Inc.; United States; Remote; USD 83,000 - 165,000; Part time

Job summary

micro1 Inc. is seeking Machine Learning Engineers for a global, fully remote contractor role requiring 15+ hours per week. You will craft realistic ML tasks, define evaluation criteria, and assess model-generated work to improve AI systems' performance and reliability.

Ideal candidates have paid AI-training or model-evaluation experience and strong Python skills, with hands-on use of PyTorch, JAX, TensorFlow, or related ML tools. Flexible scheduling and remote collaboration are offered.

Qualifications

• Paid experience training, evaluating, benchmarking, or improving AI systems.
• Strong practical proficiency with Python.
• Hands-on experience with PyTorch, JAX, TensorFlow, scikit-learn, XGBoost, or LightGBM.

Responsibilities

• Create realistic machine learning tasks that challenge advanced AI models.
• Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
• Evaluate model-generated code, experiments, and technical deliverables.
• Identify issues like data leakage or unstable training and improve reproducibility.

Skills

Python

AI model evaluation

Task design

Tools

PyTorch

JAX

TensorFlow

scikit-learn

XGBoost

LightGBM

Job description

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 Machine Learning Engineers with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems.

You will use your machine learning expertise to create challenging technical tasks, evaluate model-generated work, identify failures, and establish objective standards for correct solutions. This role is intended for candidates who have already completed substantive AI-training work.

What You'll Work On

1. Create realistic machine learning engineering tasks that challenge advanced AI models.


2. Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.


3. Evaluate model-generated code, experiments, analyses, and technical deliverables.


4. Identify issues involving data leakage, incorrect validation, unstable training, performance, or reproducibility.

Required Qualifications

1. Prior paid experience training, evaluating, benchmarking, or improving AI systems.


2. Strong practical proficiency with Python.


3. 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:

1. GitHub repositories or public model implementations


2. Kaggle, DrivenData, AIcrowd, or similar competition results


3. Hugging Face models or datasets


4. Research publications or technical reports


5. Open-source ML contributions


6. Public benchmarks, experiments, or technical projects

Preferred Qualifications

1. Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.


2. Experience reviewing other AI trainers’ technical submissions.


3. Kaggle Expert, Master, or Grandmaster status, or strong results in another technical competition.


4. Publications or open-source contributions in machine learning.


5. Prior experience with long-horizon or agentic AI evaluations.


6. Availability to begin immediately.

Compensation

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