Remote AI & ML Engineer — Train & Evaluate Next-Gen LLMs

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

Job summary

Prolific is seeking an AI & Machine Learning Engineer focused on AI training to join its Expert Network. You’ll evaluate architecture logic, audit training code, and refine RLHF/RAG workflows to improve model alignment and performance.

This role emphasizes deep learning, transformers, and production-grade ML practice. Ideal candidates have hands-on experience with prompts, RLHF, and vector stores, plus strong Python and ML framework proficiency.

Qualifications

• Education: BS, MS, or PhD 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: neural networks and optimization.
• LLM specialization: Prompt Engineering, RLHF, or RAG workflows.
• Technical rigor: audit complex model logic and training data contamination.
• Analytical critique: spotting hallucinations, biased outputs, or logical failures.

Responsibilities

• Evaluate LLM architecture logic: review model explanations for technical accuracy.
• Audit code and notebooks for efficiency and correctness.
• Refine RLHF frameworks to align models with human intent and safety.
• Analyze model reasoning and chain-of-thought prompts to identify failures.
• Benchmark performance across model outputs using technical metrics.

Skills

Transformers

CNNs

RNNs

Prompt Engineering

RLHF

RAG workflows

Audit model logic

Data contamination audit

Python

NumPy

Pandas

Scikit-learn

Hugging Face Transformers

PyTorch

TensorFlow/Keras

LangChain

Pinecone

Milvus

Weaviate

Education

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

Tools

PyTorch

TensorFlow/Keras

Python

NumPy

Pandas

Scikit-learn

Hugging Face Transformers

Weights & Biases

LangChain

Pinecone

Milvus

Weaviate

Job description

Prolific is seeking an AI & Machine Learning Engineer focused on AI training to join its Expert Network. You’ll evaluate architecture logic, audit training code, and refine RLHF/RAG workflows to improve model alignment and performance.

This role emphasizes deep learning, transformers, and production-grade ML practice. Ideal candidates have hands-on experience with prompts, RLHF, and vector stores, plus strong Python and ML framework proficiency.