About the job
Join Our Internship Program in Berlin!
We are excited to announce a full-time, in-person internship opportunity in our Berlin office, lasting between 12 to 24 weeks.
Your Responsibilities
Drive the advancement of search quality utilizing various models, data, tools, and innovative techniques.
Train and fine-tune large-scale deep learning models using PyTorch, employing distributed training methods (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models.
Engage in research on representation learning, exploring contrastive learning, multilingual capabilities, evaluation methodologies, and multimodal modeling for enhanced search and retrieval.
Design and refine RAG pipelines for effective grounding and answer generation.
Qualifications
Solid understanding of search and retrieval systems, including quality evaluation metrics and principles.
Demonstrated proficiency with PyTorch, particularly in distributed training and performance optimization for large models.
A keen interest in representation learning, covering areas such as contrastive learning, dense and sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization, and robust evaluation methods.
A publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR) is a plus.

