[Hiring] AI Trainer @Xperteez Technology

Role Description

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.

• 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.

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:
• 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

Requirements

• 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.