AI/ML Engineer for LLM Training — Remote

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

Benefits offered by this job

Flexible hours

Work from home

Competitive pay rates

Job summary

Prolific is seeking AI & Machine Learning Engineers to join its Expert Network, training and evaluating next-gen LLMs. A quick 10–15 minute test may be required. If successful, you’ll be invited as a participant and paid to train and evaluate models.

Researchers pay up to $80 per hour for tasks; expect tasks that may take about an hour, though many are shorter. Strong ML/LLM experience is essential, with hands-on work in production environments.

Qualifications

• Education: BS, MS or PhD in Computer Science, Artificial Intelligence, Robotics, or related quantitative field with ML focus.
• Professional Experience: building, deploying, or fine-tuning ML models in production.

Responsibilities

• Evaluate LLM architecture logic with focus on technical accuracy of explanations, loss functions, and backpropagation.
• Audit ML training code and notebooks for efficiency and correctness.
• Refine RLHF frameworks with high-quality human feedback aligned to safety and helpfulness.
• Analyze model reasoning and identify breakdowns in chain-of-thought prompts.
• Benchmark model outputs using defined technical taxonomies and metrics.

Skills

AI/ML engineering

LLM evaluation

Neural networks

Prompt engineering

RLHF awareness

CoT analysis

Education

BS/MS/PhD in CS/AI/Robotics with ML focus

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 its Expert Network, training and evaluating next-gen LLMs. A quick 10–15 minute test may be required. If successful, you’ll be invited as a participant and paid to train and evaluate models.

Researchers pay up to $80 per hour for tasks; expect tasks that may take about an hour, though many are shorter. Strong ML/LLM experience is essential, with hands-on work in production environments.