Internship program: 12 - 24 weeks, full-time, in-person in the Berlin office.
Responsibilities
•
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.
Qualifications
•
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).