AI Engineer/ML Engineer - Senior Developers - AI Training - Louisville, US

Role overview The position invites experienced AI and machine learning engineers to join a paid expert network that supports the training and evaluation of large language models. Contributors apply deep technical judgment to assess model outputs, audit code, and provide structured human feedback that improves model alignment and reasoning quality. It is a flexible, remote participant role based in the Louisville area rather than a traditional employment position, with hourly compensation for completed tasks.

Responsibilities - Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy. - Audit ML code and notebooks, including training loops, preprocessing scripts, and evaluation routines, for correctness and efficiency. - Supply structured human feedback used to refine RLHF processes and improve model alignment, safety, and helpfulness. - Critically evaluate how a model reasons through chain-of-thought prompts and pinpoint where the logic breaks down. - Run comparative benchmarks across model outputs against defined technical taxonomies and performance criteria. - Identify hallucinations, bias, training-data contamination, and flawed mathematical reasoning in AI-generated technical content.

Requirements - BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a machine learning focus. - Hands-on experience building, deploying, or fine-tuning machine learning models in production environments. - Professional-level knowledge of neural network architectures such as Transformers, CNNs, and RNNs, along with relevant optimization techniques. - Practical experience with prompt engineering, reinforcement learning from human feedback (RLHF), or retrieval-augmented generation (RAG) workflows. - Ability to reason about mathematical proofs behind ML algorithms and audit complex model logic. - Strong attention to detail when spotting hallucinations, biased outputs, or logical failures.

Nice to have - Expert proficiency in PyTorch or TensorFlow/Keras. - Advanced Python skills with NumPy, Pandas, Scikit-learn, and Hugging Face Transformers. - Familiarity with AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, or LangChain. - Experience with vector databases such as Pinecone, Milvus, or Weaviate for RAG evaluation.

Benefits and work setup - Hourly compensation for completed tasks, with researcher budgets reaching up to $80 per hour. - Fully remote work with flexible hours; many tasks are short, though some require roughly an hour of uninterrupted focus. - A brief skills assessment gates entry, after which contributors can begin accepting paid assignments.