AI Trainer (ML Engineering)

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

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

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

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

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

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.

Application Process

Submit your application, AI-training experience, 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.

Originally posted on Himalayas