Internship - Machine Learning Research Engineer

On-siteJuniorResearch Scientistposted 2mo ago

✦Role summaryAI-generated

The intern will develop and optimize large‑scale deep learning models for search and retrieval using PyTorch and distributed training frameworks. They will also conduct research on representation learning techniques such as contrastive, multilingual, and multimodal methods while building RAG pipelines for answer generation.

Skills required

PyTorch Deepspeed Fsdp Distributed training Contrastive learning Multilingual Multimodal Retrieval Ranking RAG Deep learning AI

About this role

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).

Apply on Perplexity →Opens in new tab

score your resume against this role