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

Role overview This is a paid participant opportunity for senior AI and machine learning engineers to help train and evaluate large language models through structured, task-based work. Rather than a traditional full-time role, contributors take on individual studies that pay by the hour and draw on deep technical expertise to shape model behavior. The mission centers on supplying the high-quality human feedback that modern alignment pipelines depend on.

Responsibilities - Audit AI-generated explanations of neural architectures, loss functions, and backpropagation for technical accuracy - Review ML code and notebooks, including training loops, data preprocessing scripts, and evaluation routines, flagging inefficiencies or bugs - Contribute high-quality human ratings used in reinforcement learning from human feedback (RLHF) workflows to steer models toward safer, more helpful outputs - Trace how a model navigates chain-of-thought prompts and pinpoint where its reasoning breaks down - Run comparative benchmarks of competing model outputs against defined taxonomies and performance metrics

Requirements - BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a machine learning focus - Professional experience building, deploying, or fine-tuning ML models in production settings - Strong grasp of neural network architectures including Transformers, CNNs, and RNNs, along with their optimization techniques - Hands-on work with prompt engineering, RLHF, or retrieval-augmented generation (RAG) systems - Ability to audit complex model logic, detect training data contamination, and assess the mathematical underpinnings of ML algorithms - Sharp analytical eye for hallucinations, biased outputs, and logical failures in AI-generated technical content

Nice to have - Expert proficiency in PyTorch or TensorFlow and Keras - Advanced Python skills with NumPy, Pandas, and Scikit-learn, plus experience using Hugging Face Transformers - Familiarity with cloud ML platforms such as AWS SageMaker or Google Vertex AI, and tooling like Weights and Biases or LangChain - Experience with vector databases such as Pinecone, Milvus, or Weaviate for RAG evaluation

Benefits and work setup - Hourly compensation that can reach up to $80 depending on the study - Fully remote work with flexible scheduling - Short 10 to 15 minute skills assessment followed by fast onboarding - Most tasks fit within roughly one hour of focused, uninterrupted work, with many shorter in length