Internship - Machine Learning Research Engineer (Berlin)
Role Overview
This is a full-time, in-person internship role for a Machine Learning Research Engineer in Berlin, focusing on advancing search quality through deep learning models and research. Day-to-day responsibilities include training and optimizing large-scale models using PyTorch with distributed techniques, conducting research in representation learning for search and retrieval, and building RAG pipelines. The hire will work on a research-oriented team, contributing to impactful projects in AI-driven search systems.
Perks & Benefits
The internship offers a 12-24 week program in Berlin, providing hands-on experience with cutting-edge AI technologies and frameworks like PyTorch and DeepSpeed. While the job posting mentions remote location, it specifies in-person work in Berlin, implying a collaborative office environment with potential for career growth through research and publication opportunities. Benefits likely include exposure to industry-leading projects and networking in a tech-focused setting, typical for such roles.
Full Job Description
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).
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