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Perplexity

Internship - Machine Learning Research Engineer

Perplexity Berlin, Germany

Software Development · 201-500 employees

Sep 02
Remote machine-learning Junior (0-2 yrs) Full-time Germany
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About the role

The intern will focus on advancing search quality by training and optimizing large-scale deep learning models using PyTorch. Additionally, they will conduct research in representation learning and build RAG pipelines for grounding and answer generation.

What they look for

Machine Learning Deep Learning PyTorch Distributed Training Search Systems Retrieval Systems Representation Learning Contrastive Learning Multimodal Modeling RAG Pipelines Data Optimization Performance Optimization Vector Representations Cross-lingual Representation Ranking Models

Requirements

Candidates should have a strong understanding of search and retrieval systems and proficiency in PyTorch with experience in distributed training. A publication record in AI/ML conferences or workshops is also required.

Full 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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