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This internship program focuses on advancing search quality through deep learning models, research, and RAG pipeline development.

Advance search quality through models, data, tools, or other available leverage.
Train and optimize large-scale deep learning models using PyTorch, distributed training, and hardware acceleration, focusing on retrieval and ranking models.
Conduct research in representation learning, including contrastive learning, multilingual, evaluation, and multimodal modeling for search and retrieval.
Build and optimize RAG pipelines for grounding and answer generation.

Understanding of search and retrieval systems, including quality evaluation principles and metrics.
Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models.
Interest in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization and robust evaluation.
Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).

Internship - Machine Learning Research Engineer at Perplexity

Internship - Machine Learning Research Engineer