AI Engineer​/ML Engineer - Senior Developers - AI Training - Atlanta,

AI & Machine Learning Engineer – AI Training

This role focuses on training and evaluating large language models, leveraging deep machine learning expertise.

• Education: a BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning.
• Professional
  
  Experience:
  
  experience building, deploying, or fine‑tuning ML models in a production environment.
• Deep Learning Mastery: professional-level understanding of neural network architectures (Transformers, CNNs, RNNs) and optimization techniques.
• LLM Specialization: hands‑on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval‑Augmented Generation) workflows.
• Technical Rigor: the ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms.
• Analytical Critique: high attention to detail in spotting hallucinations, biased outputs, or logical failures in AI-generated technical content.

• Evaluate LLM Architecture Logic: review AI‑generated explanations of model architectures, loss functions, and back propagation for technical accuracy.
• Audit Code & Notebooks: validate ML‑specific code (e.g., training loops, data preprocessing scripts, or model evaluations) for efficiency and correctness.
• Refine RLHF Frameworks: provide high‑quality human feedback necessary to align models with human intent, safety, and helpfulness.
• Analyze Model Reasoning: critically assess how an AI model navigates complex chain‑of‑thought prompts and identify where the reasoning breaks down.
• Benchmark Performance: conduct comparative testing between different model outputs based on specific technical taxonomies and performance metrics.

• Frameworks: expert proficiency in PyTorch or Tensor Flow/Keras.
• Language & Data: advanced Python (Num Py, Pandas, Scikit‑learn) and experience with Hugging Face Transformers.
• Cloud & MLOps: experience with AWS (Sage Maker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and Lang Chain.
• Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation.

Competitive pay rates, flexible hours, and the ability to work from home.

We provide a platform that connects researchers and companies with a global pool of participants, enabling the collection of high‑quality, ethically sourced human behavioural data and feedback.

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