Internship - Machine Learning Research Engineer

Internship Program Berlin 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).

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).

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.