AI/ML Engineer for LLM Training - Remote, Flexible Hours

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

Benefits offered by this job

Flexible hours

Work from home

Competitive pay

Job summary

Prolific is seeking AI & Machine Learning Engineers to join its Expert Network to train and evaluate the next generation of LLMs. You’ll review architectures, audit code, and refine RLHF workflows while earning competitive pay for hourly tasks completed remotely.

Ideal candidates have deep learning mastery, hands-on ML deployment experience, and strong knowledge of Transformers, RLHF, and RAG approaches. Flexible hours and home-based work are supported.

Qualifications

• Education: a BS, MS, or PhD in Computer Science, AI, Robotics, or related quantitative field with a focus on ML.
• Professional experience: building, deploying, or fine-tuning ML models in production.
• Deep learning mastery: neural networks architectures and optimization techniques.
• LLM specialization: Prompt Engineering, RLHF, or RAG workflows.
• Technical rigor: audit model logic, data contamination, and proofs behind ML algorithms.
• Analytical critique: identify hallucinations or biased outputs in AI content.

Responsibilities

• Evaluate LLM architecture logic and 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 CoT prompts to identify breakdowns.
• Benchmark model outputs using defined metrics and taxonomies.

Skills

Transformers

CNNs

RNNs

Prompt Engineering

RLHF

RAG

Model Auditing

Chain-of-Thought

Data Contamination

Education

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

Tools

PyTorch

TensorFlow/Keras

Hugging Face Transformers

AWS SageMaker

Google Vertex AI

Weights & Biases

LangChain

Pinecone

Milvus

Weaviate

Job description

Prolific is seeking AI & Machine Learning Engineers to join its Expert Network to train and evaluate the next generation of LLMs. You’ll review architectures, audit code, and refine RLHF workflows while earning competitive pay for hourly tasks completed remotely.

Ideal candidates have deep learning mastery, hands-on ML deployment experience, and strong knowledge of Transformers, RLHF, and RAG approaches. Flexible hours and home-based work are supported.