AI Trainer (ML Engineering)

Overview

micro1 is seeking machine learning engineers with prior professional AI-training or model-evaluation experience to help improve advanced AI systems. This contractor role is fully remote, open globally, and paid per accepted task. The work is intended for candidates who have already completed substantive AI-training work.

Compensation: $60–$120/hour equivalent, paid per accepted task. Compensation is output-based, and task availability and completion time may vary.

Time commitment: 15+ hours per week, with a flexible schedule.

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 related to data leakage, incorrect validation, unstable training, performance, and reproducibility.

Requirements

• Prior paid experience training, evaluating, benchmarking, or improving AI systems.
• Strong practical proficiency with Python.
• Hands-on experience with PyTorch, JAX, TensorFlow, scikit-learn, XGBoost, LightGBM, or similar ML tools.
• Applicants should be able to provide public proof of skill such as GitHub repositories, public model implementations, Kaggle/DrivenData/AIcrowd results, Hugging Face models or datasets, research publications, technical reports, open-source contributions, or public benchmarks and experiments.

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 equivalent technical competition results.
• Publications or open-source contributions in machine learning.
• Prior experience with long-horizon or agentic AI evaluations.
• Availability to begin immediately.

Application Process

1. Submit your application, AI-training experience, and public technical evidence.
2. Complete an AI interview with technical and integrity checks.
3. Complete a hiring-manager review.
4. If selected, finish project onboarding and begin your first assigned task.