AI Engineer/ML Engineer - Senior Developers - AI Training - San Francisco, US

Evaluate LLM Architecture Logic

Review AI-generated explanations of model architectures, loss functions, and backpropagation 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 the 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 (CoT) 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.

Key Technologies

• Frameworks: expert proficiency in PyTorch or TensorFlow/Keras.

• Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers.

• Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain.

• Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation.