Remote AI & ML Engineer — LLM Training & Evaluation

Prolific; United States; Remote; USD 66,000 - 110,000; Full time

Job summary

Prolific is seeking AI & Machine Learning Engineers to join our Expert Network to train and evaluate next-generation LLMs. You’ll work on deep technical tasks, including model evaluation, RLHF refinement, and prompt engineering, with paid tasks that test your expertise.

This role offers remote, flexible hours, and pay up to $80 per hour for experienced engineers who can audit code, assess model reasoning, and benchmark architectures using PyTorch or TensorFlow.

Qualifications

• Education: BS, MS or PhD in Computer Science, AI, Robotics or related quantitative field with ML focus.
• Professional experience building, deploying, or fine-tuning ML models in production.
• Deep learning mastery in transformers, CNNs, RNNs with optimization techniques.
• LLM specialization: Prompt Engineering, RLHF or RAG workflows.
• Technical rigor: audit model logic and data, evaluate proofs and training data.

Responsibilities

• Evaluate LLM architecture logic and verify technical accuracy of explanations.
• Audit ML code and notebooks for efficiency and correctness.
• Refine RLHF frameworks with high-quality human feedback.
• Analyze model reasoning and identify failure points in CoT prompts.
• Benchmark model outputs using defined technical taxonomies and metrics.

Skills

AI/ML expertise

Production ML experience

Attention to detail

CoT reasoning critique

Prompt engineering

Education

BS/MS/PhD in CS/AI/Robotics

Tools

PyTorch

TensorFlow/Keras

Python

Hugging Face

RAG workflows

RLHF

Job description

Prolific is seeking AI & Machine Learning Engineers to join our Expert Network to train and evaluate next-generation LLMs. You’ll work on deep technical tasks, including model evaluation, RLHF refinement, and prompt engineering, with paid tasks that test your expertise.

This role offers remote, flexible hours, and pay up to $80 per hour for experienced engineers who can audit code, assess model reasoning, and benchmark architectures using PyTorch or TensorFlow.