Machine Learning Research Engineer Internship

About the role

We are looking for a motivated intern to join our Berlin team for a 12‑24 week full‑time, in‑person internship. The role focuses on advancing search quality through cutting‑edge machine‑learning research and development.

Key responsibilities

• Push search quality forward by developing, training, and optimizing large‑scale deep learning models using PyTorch and distributed training frameworks.
• Research representation learning techniques such as contrastive learning, multilingual and multimodal modeling for retrieval and ranking.
• Build and optimize Retrieval‑Augmented Generation (RAG) pipelines for grounding and answer generation.
• Evaluate model performance using search quality metrics and contribute to data and tool improvements.

Required profile

• Understanding of search and retrieval systems, including quality evaluation principles and metrics.
• Strong proficiency with PyTorch and experience in distributed training techniques and performance optimization for large models.
• Interest in representation learning, including contrastive learning, dense & sparse vector representations, and cross‑lingual alignment.
• Track record of publications in AI/ML conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, or SIGIR.

Required skills

• PyTorch
• Distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP)
• Contrastive learning
• Multilingual and multimodal representation modeling
• RAG pipeline development