LLM AI Training & Evaluation Engineer (Remote)

Prolific Academic Ltd; United States; Remote; USD 83,000 - 110,000; Full time

Benefits offered by this job

Flexible hours

Work from home

Competitive pay up to $80/hr

Job summary

Prolific is seeking AI & Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you pass a short test, you’ll be invited to participate and get paid to work on AI tasks.

Roles pay up to $80 per hour, with flexible hours and the option to work from home. Candidates should have a quantitative degree and hands-on experience with ML models, RLHF, or RAG workflows.

Qualifications

• BS, MS, or PhD in Computer Science, AI, Robotics, or a related quantitative field with ML focus.
• Experience building, deploying, or fine-tuning ML models in production.
• Deep learning mastery over Transformers, CNNs, RNNs.
• Hands-on experience with RLHF or RAG workflows.
• Ability to audit model logic and identify data contamination.

Responsibilities

• Evaluate LLM architecture logic and assess technical accuracy.
• Audit ML code and notebooks for efficiency and correctness.
• Refine RLHF frameworks and collect high-quality human feedback.
• Analyze model reasoning and identify where it breaks down.
• Benchmark model outputs against technical taxonomies and metrics.

Skills

ML model deployment

Deep learning architectures

Prompt Engineering / RLHF

Model evaluation & auditing

Analytical reasoning for AI outputs

Education

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

Tools

PyTorch

TensorFlow/Keras

Hugging Face Transformers

AWS SageMaker

Vertex AI

Weights & Biases

LangChain

Pinecone/Milvus/Weaviate

Job description

Prolific is seeking AI & Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you pass a short test, you’ll be invited to participate and get paid to work on AI tasks.

Roles pay up to $80 per hour, with flexible hours and the option to work from home. Candidates should have a quantitative degree and hands-on experience with ML models, RLHF, or RAG workflows.