AI Trainer (ML Engineering)
micro1
Remote, Worldwide
Posted
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