🌎 AI Trainer (ML Engineering), Remote- Contract

Role Title: AI Trainer (ML Engineering) Location: Global, fully remote Job Type: Contract Time Commitment: 15+ hours per week Schedule: Flexible Required Skills: ML Engineering AI training Programming Skills 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