Remote AI/ML Engineer - LLM Training & Evaluation

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

Benefits offered by this job

Competitive pay

Flexible hours

Work from home

Job summary

Prolific is seeking AI & Machine Learning Engineers to join our Expert Network to train and evaluate the next generation of LLMs using deep technical expertise. If successful, you will join Prolific as a paid participant to work on paid AI tasks from home.

Ideal candidates bring a BS/MS/PhD in CS/AI or related fields, hands-on ML model development, and proficiency in PyTorch or TensorFlow. You will work with RLHF or RAG workflows and rigorous model evaluation.

Qualifications

• Education in Computer Science, AI, Robotics, or related quantitative field with ML focus.
• Experience building, deploying, or fine-tuning ML models in production.
• Deep learning mastery of neural networks (Transformers, CNNs, RNNs) and optimization techniques.
• Hands-on experience with RLHF or RAG workflows.
• Ability to audit complex model logic and detect data contamination.

Responsibilities

• Evaluate LLM architecture logic and technical accuracy of explanations.
• Audit ML code and notebooks for efficiency and correctness.
• Refine RLHF frameworks to align models with human intent and safety.
• Analyze model reasoning and CoT prompts to identify failure modes.
• Benchmark model outputs using technical taxonomies and metrics.

Skills

ML model deployment

Transformer architectures

Prompt Engineering

RLHF / RAG workflows

Model evaluation

Code & notebook audit

Education

BS/MS/PhD in CS/AI/related field

Tools

PyTorch

TensorFlow/Keras

Python (NumPy, Pandas)

Hugging Face Transformers

LangChain

Job description

Prolific is seeking AI & Machine Learning Engineers to join our Expert Network to train and evaluate the next generation of LLMs using deep technical expertise. If successful, you will join Prolific as a paid participant to work on paid AI tasks from home.

Ideal candidates bring a BS/MS/PhD in CS/AI or related fields, hands-on ML model development, and proficiency in PyTorch or TensorFlow. You will work with RLHF or RAG workflows and rigorous model evaluation.