Member of Technical Staff (machine Learning Research Engineer)

at Perplexity · AI Frontier · Berlin, Germany · Search

Seeking an ML Research Engineer to advance search technologies, focusing on retrieval and ranking. Responsibilities include architecting and building core search platform components, designing/training/optimizing large-scale deep learning models (PyTorch, distributed training), conducting research in representation learning (contrastive, multilingual, multimodal), deploying models, and building RAG pipelines. Requires deep understanding of search/retrieval, large-scale search/recommender systems experience, PyTorch proficiency, expertise in representation learning, and a strong publication record.

What you'd actually do

  1. Relentlessly push search quality forward — through models, data, tools, or any other leverage available
  2. Architect and build core components of the search platform and model stack
  3. 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
  4. Conduct advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval
  5. Deploy models — from boosting algorithms to LLMs — in a scalable and performant way
  6. Build and optimize RAG pipelines for grounding and answer generation

Skills

Required

  • PyTorch
  • distributed training
  • performance optimization
  • representation learning
  • contrastive learning
  • multilingual modeling
  • multimodal modeling
  • search systems
  • retrieval systems
  • quality evaluation principles and metrics
  • large-scale search or recommender systems
  • embedding space alignment

Nice to have

  • DeepSpeed
  • FSDP
  • hardware acceleration
  • LLMs

What the JD emphasized

  • Proven track record with large-scale search or recommender systems
  • Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR)

Other signals

  • building core search components
  • optimizing large-scale deep learning models
  • deploying models at scale
  • building and optimizing RAG pipelines
Read full job description

Perplexity is seeking an experienced Machine Learning Research 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