AI Trainer (ML Engineering)

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

• 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.