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).
As a PhD Machine Learning Engineer Intern, you will tackle open research problems, develop ML models, and collaborate with cross-functional teams to enhance Lyft's ML platform.
As a Machine Learning Intern, you'll apply machine learning and AI to product challenges, collaborating with teams to prototype and improve ML-powered features.
Build and evaluate machine-learning models, semantic search, and governed language-model applications for life-sciences quality and manufacturing, contributing across data preparation, experimentation, and production integration.
As a Machine Learning Intern, you'll apply machine learning and AI to product challenges, collaborating with teams to prototype and improve ML-powered features.
As a Machine Learning Intern, you'll apply machine learning and AI to product challenges, collaborating with teams to prototype and improve ML-powered features.
