AI & ML Engineer for LLM Training (Remote)

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

Job summary

Prolific is seeking AI and Machine Learning Engineers to join our Expert Network and help train and evaluate the next generation of LLMs. If successful, you’ll be invited to join Prolific as a participant, where you’ll get paid to train and evaluate powerful AI models from home with flexible hours.

In this role you’ll evaluate LLM architecture, audit code and notebooks, refine RLHF frameworks, analyze model reasoning, and benchmark performance using technical taxonomies and metrics.

Qualifications

• Advanced degree in CS/AI/Robotics or related ML-focused field.
• Hands-on experience building, deploying, or fine-tuning ML models in production.
• Strong understanding of transformer architectures and optimization techniques.
• Experience with RLHF or RAG workflows in real projects.
• Ability to audit complex model logic and detect data issues.

Responsibilities

• Evaluate LLM architecture logic and assess technical accuracy of explanations.
• Audit ML code and notebooks for efficiency and correctness.
• Refine RLHF frameworks to align models with human intent and safety.
• Analyze model reasoning and identify points where CoT prompts fail.
• Benchmark model outputs using specific metrics and taxonomies.

Skills

ML model deployment

Deep learning

LLM expertise

Prompt engineering

RLHF

RAG workflows

Model auditing

Error analysis

Education

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

Tools

PyTorch

TensorFlow/Keras

Python (NumPy, Pandas, Scikit-learn)

Hugging Face Transformers

AWS SageMaker

Google Vertex AI

Weights & Biases

LangChain

Pinecone/Milvus/Weaviate

Job description

Prolific is seeking AI and Machine Learning Engineers to join our Expert Network and help train and evaluate the next generation of LLMs. If successful, you’ll be invited to join Prolific as a participant, where you’ll get paid to train and evaluate powerful AI models from home with flexible hours.

In this role you’ll evaluate LLM architecture, audit code and notebooks, refine RLHF frameworks, analyze model reasoning, and benchmark performance using technical taxonomies and metrics.