Write and test product or system development code. Develop representation learning models, including embedding or encoder models trained with contrastive, supervised, or self-supervised (e.g., masked-prediction) objectives, and set or sequence encoders (attention pooling, Transformers). Execute clustering or metric learning at scale (e.g., hierarchical/agglomerative, density-based), and assess cluster quality. Utilize Large Language Model (LLMs) in ML systems (e.g., LLM embeddings, autoraters for evaluation) and distill LLM judgments into classifiers.
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