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Internship program: 12 - 24 weeks, full-time, in-person in the Berlin office.
Job Responsibility:
Relentlessly push search quality forward — through models, data, tools, or any other leverage available
Train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus 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
Requirements:
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
Interested 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)