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

Role overview This is a paid participant opportunity for senior AI and machine learning engineers based in Memphis who want to channel their production ML experience into evaluating and refining frontier large language models. The role emphasizes deep technical judgment, code auditing, and analytical critique rather than model development. It suits engineers seeking flexible, remote-friendly work that fits alongside other commitments.

Responsibilities - Scrutinize AI-generated explanations of architectures, loss functions, and backpropagation for accuracy. - Audit ML code, including training loops, data preprocessing, and evaluation notebooks, flagging inefficiencies and bugs. - Deliver high-quality feedback that fuels RLHF processes targeting intent alignment, safety, and helpfulness. - Trace AI reasoning through chain-of-thought prompts to locate points of logical failure. - Conduct comparative benchmarks across model outputs using defined taxonomies and quantitative metrics.

Requirements - BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a quantitative field with a machine learning concentration. - Professional experience building, deploying, or fine-tuning ML systems in production. - Deep working knowledge of neural network architectures including Transformers, CNNs, RNNs, and associated optimization methods. - Hands-on involvement with prompt engineering, RLHF, or retrieval-augmented generation workflows. - Careful analytical eye for catching hallucinations, biased outputs, or mathematically unsound claims in AI-generated material.

Nice to have - Expert command of PyTorch or TensorFlow/Keras, plus strong Python skills with NumPy, Pandas, and Scikit-learn. - Experience with Hugging Face Transformers and vector stores such as Pinecone, Milvus, or Weaviate. - Familiarity with cloud MLOps platforms such as AWS SageMaker, Vertex AI, Weights & Biases, or LangChain.

Benefits and work setup - Hourly rates up to $80 depending on task complexity and researcher budget. - Remote work with flexible hours and paid tasks typically under one uninterrupted hour. - Streamlined onboarding following a short technical assessment.