AI & ML Engineer for LLM Training — Remote

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

Benefits offered by this job

Competitive pay rates

Flexible hours

Work from home

Job summary

Prolific is seeking AI & Machine Learning Engineers to join its Expert Network for training and evaluating next-gen LLMs. You will complete a shortskills test and, if successful, participate as a paid contributor in AI tasks with flexible hours and work-from-home options.

Ideal candidates hold a BS/MS/PhD in a quantitative field and have hands-on experience in deploying or fine-tuning ML models, with expertise in transformers, RLHF, or RAG workflows.

Qualifications

• BS, MS, or PhD in CS/AI/Robotics or related field with ML focus.
• Experience building, deploying, or fine-tuning ML models in production.
• Deep learning architectures (Transformers, CNNs, RNNs) mastery.
• LLM specialization: Prompt Engineering, RLHF, or RAG workflows.
• Audit complex model logic and data contamination; evaluate mathematical proofs behind ML algorithms.
• Analytical critique: detect hallucinations and biases in AI outputs.

Responsibilities

• Evaluate LLM architecture logic for technical accuracy.
• Audit ML code and notebooks for efficiency and correctness.
• Refine RLHF frameworks with high-quality human feedback.
• Analyze model reasoning and identify where chain-of-thought fails.
• Benchmark model outputs using technical taxonomies and metrics.

Skills

Deep learning mastery

LLM workflow experience

Prompt engineering

Model evaluation

Code auditing

Education

BS/MS/PhD in CS/AI/Robotics or related field with ML focus

Tools

PyTorch

TensorFlow/Keras

Hugging Face

SageMaker

Vertex AI

Weights & Biases

LangChain

Pinecone

Job description

Prolific is seeking AI & Machine Learning Engineers to join its Expert Network for training and evaluating next-gen LLMs. You will complete a shortskills test and, if successful, participate as a paid contributor in AI tasks with flexible hours and work-from-home options.

Ideal candidates hold a BS/MS/PhD in a quantitative field and have hands-on experience in deploying or fine-tuning ML models, with expertise in transformers, RLHF, or RAG workflows.