Perplexity

Machine Learning Research Engineer

Perplexity

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Job Level

Mid-LevelSenior

Tech Stack

PyTorch

About the role

  • Perplexity is seeking an experienced Senior Machine Learning Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking.
  • Responsibilities: Relentlessly push search quality forward — through models, data, tools, or any other leverage available
  • Architect and build core components of the search platform and model stack
  • Design, 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 advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval
  • Deploy models — from boosting algorithms to LLMs — in a scalable and performant way
  • Build and optimize RAG pipelines for grounding and answer generation
  • Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery
  • Qualifications: Deep understanding of search and retrieval systems, including quality evaluation principles and metrics
  • Proven track record with large-scale search or recommender systems
  • Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models
  • Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications
  • Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR)
  • Self-driven, with a strong sense of ownership and execution
  • Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas

Requirements

  • Deep understanding of search and retrieval systems, including quality evaluation principles and metrics
  • Proven track record with large-scale search or recommender systems
  • Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models
  • Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications
  • Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR)
  • Self-driven, with a strong sense of ownership and execution
  • Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas