AI Trainer (ML Engineering)

Frontier AI Lab; United States; A distancia; USD 83.000 - 165.000; A tiempo parcial

Descripción de la vacante

Remotebridge is seeking Machine Learning Engineers on a contractor basis to design challenging AI tasks and evaluate model outputs. You will build instructions, environments, and clear evaluation criteria for rigorous assessment.

The role requires prior AI-training or model-evaluation experience and strong Python proficiency, with hands-on use of PyTorch, TF, and related ML tools. Remote, flexible schedule, 15+ hours weekly.

Formación

• 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 comparable ML tools.

Responsabilidades

• 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.

Conocimientos

Python

ML evaluation

Model evaluation

ML benchmarking

Herramientas

PyTorch

JAX

TensorFlow

scikit-learn

XGBoost

LightGBM

Descripción del empleo

Pay: $60–$120/hour equivalent, paid per accepted task

Location: Global, fully remote

Job Type: Contractor

Time Commitment: 15+ hours per week

Schedule: Flexible

Our team 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.