AI Engineer​/ML Engineer - Senior Developers - AI Training - Sacramento

Role overview

This is a participant-based opportunity for seasoned AI and machine learning engineers to contribute to the training and evaluation of large language models. Rather than a traditional full-time position, contributors complete paid research tasks on a flexible, remote schedule, applying deep technical expertise to improve model quality. The arrangement suits senior engineers who can dedicate focused, uninterrupted blocks of time to technical review work and are based in or near Sacramento.

• Assess AI-generated explanations of model architectures, loss functions, and back propagation for technical soundness.
• Audit machine learning code and notebooks, including training loops, preprocessing scripts, and evaluation pipelines.
• Provide structured human feedback used in reinforcement learning from human feedback (RLHF) pipelines to support model alignment with user intent, safety, and helpfulness.
• Examine how models handle complex chain-of-thought prompts and pinpoint where their reasoning breaks down.
• Run comparative benchmarks across competing model outputs using defined technical taxonomies and performance metrics.

• Bachelor's, Master's, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative discipline with a machine learning focus.
• Hands-on experience building, deploying, or fine-tuning ML models in production settings.
• Working command of neural network architectures such as Transformers, CNNs, and RNNs, along with relevant optimization techniques.
• Practical exposure to prompt engineering, RLHF workflows, or retrieval-augmented generation (RAG).
• Strong attention to detail for spotting hallucinations, biased outputs, or logical failures in AI-generated technical content.
• Ability to review mathematical proofs behind ML algorithms and identify potential training data contamination.

• Expert proficiency with PyTorch or Tensor Flow/Keras.
• Advanced Python using Num Py, Pandas, Scikit-learn, and Hugging Face Transformers.
• Familiarity with cloud ML platforms such as AWS Sage Maker or Google Vertex AI, or tooling like Weights & Biases and Lang Chain.
• Exposure to vector databases such as Pinecone, Milvus, or Weaviate for RAG evaluation.

• Compensation reaching up to $80 per hour for qualified contributors.
• Fully remote engagement with flexible scheduling and the ability to work from home.
• Onboarding begins with a short 10 to 15 minute skills assessment before paid tasks are offered.
• Individual tasks generally require one uninterrupted hour, with many sessions shorter than that.