P
Perplexityvia Ashby
Member of Technical Staff (Machine Learning Research Engineer)
BerlinPosted 1mo ago
ResearchStaff+Full-time#ai-lab
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About the Role
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
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
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