Machine Learning Research Engineer Intern
ML research internship focused on improving search and retrieval through deep learning, representation learning, and RAG systems. The role requires strong PyTorch and distributed-training skills, plus familiarity with search evaluation and AI/ML research publications.
About the job
Responsibilities
- Improve search quality through models, data, tools, and other high-leverage approaches.
- Train and optimize large-scale deep learning models using PyTorch, distributed training, and hardware acceleration, with emphasis on retrieval and ranking models.
- Conduct research in representation learning, including contrastive learning, multilingual modeling, evaluation, and multimodal modeling for search and retrieval.
- Build and optimize retrieval-augmented generation (RAG) pipelines for grounding and answer generation.
Requirements
- Understanding of search and retrieval systems, including quality evaluation principles and metrics.
- Strong proficiency with PyTorch, distributed training techniques, and performance optimization for large models.
- Interest in representation learning, including contrastive learning, dense and sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization, and robust evaluation.
- Publication record in AI/ML conferences or workshops such as NeurIPS, ICML, ICLR, ACL, EMNLP, or SIGIR.
Program Details
- 12–24-week, full-time internship program.
- In-person work in the Berlin office.
Skills
PyTorch, Deep Learning, Distributed Training, Pytorch Distributed, Deepspeed, Fsdp, Hardware Acceleration, Retrieval Systems, Ranking Models, Representation Learning, Contrastive Learning, RAG
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