AI Engineer/ML Engineer - Senior Developers - AI Training - Glasgow, UK

Role overview An expert contributor network is recruiting experienced AI and Machine Learning Engineers to help train and rigorously evaluate large language models. Contributors receive paid technical tasks that leverage real-world ML production experience, with most sessions designed to fit within an hour of focused work. The arrangement is fully remote, asynchronous, and built to accommodate practicing engineers.

Responsibilities - Evaluate AI-produced explanations of model architectures, loss functions, and backpropagation for technical soundness - Inspect ML code and notebooks, including training pipelines, preprocessing logic, and evaluation scripts, for efficiency and correctness - Provide high-quality human feedback used in RLHF training to improve model alignment with human intent - Trace chain-of-thought reasoning and flag specific points where model logic collapses - Design and execute comparative benchmark tests across model outputs against defined taxonomies and metrics

Requirements - Degree (BS, MS, or PhD) in Computer Science, Artificial Intelligence, Robotics, or another quantitative discipline with a machine learning emphasis - Demonstrated experience building, deploying, or fine-tuning ML models in production settings - Deep expertise in neural network architectures (Transformers, CNNs, RNNs) and contemporary optimization methods - Practical experience with prompt engineering, RLHF, or retrieval-augmented generation workflows - Strong technical rigor when auditing model logic, spotting training data contamination, and evaluating mathematical foundations of algorithms - Meticulous attention to detail for identifying hallucinations, biased outputs, or reasoning failures

Nice to have - Expert-level command of PyTorch or TensorFlow/Keras - Advanced Python fluency with NumPy, Pandas, and Scikit-learn, plus Hugging Face Transformers experience - Cloud and MLOps exposure with AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, or LangChain - Familiarity with vector stores such as Pinecone, Milvus, or Weaviate for evaluating RAG pipelines

Benefits and work setup - Competitive hourly rates, with researcher-driven tasks typically paying up to $80 per hour - Remote-first, flexible schedule; contributors log in and pick up tasks as available - Lightweight onboarding: a 10-15 minute assessment unlocks the contributor pool, with most tasks capped at roughly one hour of uninterrupted work