Internship - Machine Learning Research Engineer (Berlin)
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The role
Relentlessly push search quality forward through models, data, tools, or any other leverage available.
Train and optimize large-scale deep learning models using PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking.
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.
Develop a deep understanding of quality metrics for search and retrieval systems and translate insights into actionable model improvements.
A publication record in AI/ML conferences or workshops is valued; strong collaboration and communication are expected.
Train and optimize large-scale deep learning models using PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking.
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.
Develop a deep understanding of quality metrics for search and retrieval systems and translate insights into actionable model improvements.
A publication record in AI/ML conferences or workshops is valued; strong collaboration and communication are expected.
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