📋 Description

• Relentlessly push search quality forward — through models, data, tools, or any other leverage
• Train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging
• Conduct research in representation learning, including contrastive learning, multilingual
• Build and optimize RAG pipelines for grounding and answer generation.

🎯 Requirements

• Understanding of search and retrieval systems, including quality evaluation principles and metrics.
• Strong proficiency with PyTorch, including experience in distributed training techniques and
• Interested in representation learning, including contrastive learning, dense & sparse vector
• Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).