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

Role overview

This participant-focused opportunity is designed for senior AI and machine learning engineers who want to help train and evaluate cutting-edge large language models through paid research tasks. Contributors work remotely with flexible scheduling, applying professional judgment to model review, alignment, and benchmarking. The arrangement fits experienced Seattle-area engineers who can deliver focused, uninterrupted technical analysis.

Responsibilities

- Review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy. - Validate machine learning code and notebooks, including training loops, data preprocessing scripts, and evaluation logic. - Deliver high-quality human feedback used in reinforcement learning from human feedback (RLHF) pipelines for model alignment. - Examine how models respond to complex chain-of-thought prompts and locate specific reasoning breakdowns. - Perform comparative benchmarking across model outputs according to 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 concentration. - Professional experience building, deploying, or fine-tuning ML models in production settings. - Working command of neural network architectures including Transformers, CNNs, and RNNs, and associated optimization techniques. - Hands-on familiarity with prompt engineering, RLHF, or retrieval-augmented generation (RAG) workflows. - Strong attention to detail when identifying hallucinations, biased outputs, or logical failures in AI-generated technical content. - Ability to audit complex model logic, evaluate mathematical proofs behind ML algorithms, and detect training data contamination.

Nice to have

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

Benefits and work setup

- Compensation up to $80 per hour for qualifying contributors. - Fully remote work with flexible scheduling and the ability to work from home. - Onboarding through a brief 10 to 15 minute skills assessment. - Most tasks require one hour of uninterrupted focus, with many sessions shorter in duration.