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

Role overview This opportunity invites experienced AI and machine learning engineers to contribute to training and evaluating next-generation large language models. Work is performed remotely as part of an expert network, where participants apply deep technical knowledge to review, audit, and refine AI outputs. The arrangement suits independent professionals based in or near Milwaukee who want flexible, paid engagements focused on advancing model quality.

Responsibilities - Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy - Audit machine learning code and notebooks, including training loops, data preprocessing scripts, and evaluation pipelines - Provide high-quality human feedback used in reinforcement learning from human feedback (RLHF) workflows to align model behavior with human intent - Critically evaluate chain-of-thought reasoning, identifying where AI model logic breaks down or produces flawed outputs - Conduct comparative benchmarking of model outputs against defined technical 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 - Hands-on experience building, deploying, or fine-tuning ML models in production environments - Professional-level understanding of neural network architectures such as Transformers, CNNs, and RNNs, plus modern optimization techniques - Practical experience with prompt engineering, RLHF, or retrieval-augmented generation (RAG) workflows - Strong analytical rigor with the ability to audit complex model logic, detect training data contamination, and evaluate mathematical proofs - Attention to detail in identifying hallucinations, biased outputs, and logical failures in AI-generated technical content

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

Benefits and work setup - Remote participation from home with flexible hours - Competitive hourly rates up to $80 per hour, depending on the task - Assignments typically require up to one hour of uninterrupted work, with many shorter engagements - Quick onboarding after passing a short skills assessment