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

Prolific Academic Ltd; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Prolific is seeking AI & Machine Learning Engineers to join our Expert Network, helping train and evaluate next‑gen LLMs with deep technical expertise. You’ll complete a brief test to assess fit and may be invited to join Prolific as a paid participant.

Applicants should hold a BS/MS/PhD in a quantitative field and have hands‑on ML model experience, especially with Transformers, RLHF, or RAG workflows. Remote work from anywhere is supported.

Qualifications

• BS/MS/PhD in Computer Science, AI, Robotics, or related quantitative field with ML focus.
• Hands-on experience building, deploying, or fine-tuning ML models in production.
• Deep learning architectures: Transformers, CNNs, RNNs.
• LLM workflows: Prompt Engineering, RLHF, or RAG.
• Auditing model logic, data contamination, and mathematical proofs behind ML algorithms.
• Ability to spot hallucinations and biases in AI outputs.

Responsibilities

• Evaluate LLM architecture logic and technical accuracy.
• Audit ML code and notebooks for efficiency and correctness.
• Refine RLHF frameworks with high-quality human feedback.
• Analyze model reasoning and loop where it breaks down.
• Benchmark performance across model outputs and metrics.

Skills

Deep learning

Transformers

RLHF (Reinforcement Learning fromHuman

RAG (Retrieval-Augmented Generation)

ML production & deployment

Education

BS/MS/PhD in CS/AI/Robotics

Tools

PyTorch

TensorFlow/Keras

Hugging Face Transformers

AWS SageMaker

Vertex AI

Weights & Biases

LangChain

Job description

Prolific is seeking AI & Machine Learning Engineers to join our Expert Network, helping train and evaluate next‑gen LLMs with deep technical expertise. You’ll complete a brief test to assess fit and may be invited to join Prolific as a paid participant.

Applicants should hold a BS/MS/PhD in a quantitative field and have hands‑on ML model experience, especially with Transformers, RLHF, or RAG workflows. Remote work from anywhere is supported.