LLM Training Engineer — Remote & Flexible Hours

AI & Machine Learning Engineer – AI Training This role focuses on training and evaluating large language models, leveraging deep machine learning expertise. What You’ll Bring Education: a BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning. Professional Experience: experience building, deploying, or fine‑tuning ML models in a production environment. Deep Learning Mastery: professional-level understanding of neural network architectures (Transformers, CNNs, RNNs) and optimization techniques. LLM Specialization: hands‑on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval‑Augmented Generation) workflows. Technical Rigor: the ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms. Analytical Critique: high attention to detail in spotting hallucinations, biased outputs, or logical failures in AI-generated technical content. What You’ll Be Doing in the Role Evaluate LLM Architecture Logic: review AI‑generated explanations of model architectures, loss functions, and backpropagation for technical accuracy. Audit Code & Notebooks: validate ML‑specific code (e.g., training loops, data preprocessing scripts, or model evaluations) for efficiency and correctness. Refine RLHF Frameworks: provide high‑quality human feedback necessary to align models with human intent, safety, and helpfulness. Analyze Model Reasoning: critically assess how an AI model navigates complex chain‑of‑thought prompts and identify where the reasoning breaks down. Benchmark Performance: conduct comparative testing between different model outputs based on specific technical taxonomies and performance metrics. Key Technologies Frameworks: expert proficiency in PyTorch or TensorFlow/Keras. Language & Data: advanced Python (NumPy, Pandas, Scikit‑learn) and experience with Hugging Face Transformers. Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Benefits Competitive pay rates, flexible hours, and the ability to work from home. We provide a platform that connects researchers and companies with a global pool of participants, enabling the collection of high‑quality, ethically sourced human behavioural data and feedback. Privacy Statement By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information. #J-18808-Ljbffr Prolific