Remote ML Engineer - RLHF & Model Evaluation Expert

prolificacademicltd; United States; Remote; USD 55,000 - 110,000; Part time

Benefits offered by this job

Competitive hourly rates

Remote-first, flexible schedule

Lightweight onboarding

Job summary

Prolificacademicltd is seeking experienced AI/ML engineers to contribute to rigorous evaluation of large language models. Tasks are paid and designed around roughly one hour of focused work, with a fully remote, asynchronous workflow that fits a practicing engineer's schedule.

You will review model explanations, examine production ML code, provide high-quality human feedback for RLHF, and help benchmark outputs against defined taxonomies and metrics. Remote, flexible, and task-based.

Qualifications

• Degree in a quantitative discipline with ML emphasis.
• Experience building or fine-tuning ML models in production.
• Deep expertise in neural architectures and optimization methods.
• Experience with prompt engineering or RLHF workflows.

Responsibilities

• Evaluate explanations of architectures, loss functions, and backpropagation for technical soundness.
• Inspect ML code and notebooks, including training pipelines and evaluation scripts, for efficiency and correctness.
• Provide high-quality human feedback for RLHF to improve model alignment with human intent.
• Trace reasoning and flag points where model logic fails or hallucinations occur.
• Design and execute benchmark tests across model outputs using defined metrics.

Skills

ML production experience

Neural networks architectures

Prompt engineering / RLHF

Code review & debugging

Education

BS/MS/PhD in CS/ML or related field

Tools

PyTorch

TensorFlow/Keras

Hugging Face Transformers

AWS SageMaker

Job description

Prolificacademicltd is seeking experienced AI/ML engineers to contribute to rigorous evaluation of large language models. Tasks are paid and designed around roughly one hour of focused work, with a fully remote, asynchronous workflow that fits a practicing engineer's schedule.

You will review model explanations, examine production ML code, provide high-quality human feedback for RLHF, and help benchmark outputs against defined taxonomies and metrics. Remote, flexible, and task-based.