Perplexity
Member of Technical Staff (Machine Learning Research Engineer)
Berlin, DE
Role snapshot
Perplexity is hiring a Member of Technical Staff (Machine Learning Research Engineer) on a full-time basis. This is a role based in Berlin, DE. The listing highlights Search.
- Company
- Perplexity
- Location
- Berlin, DE
- Job type
- Full-time
- Experience
- 3+ years
- Posted
- Oct 2, 2026
Who this role suits
Practitioners with a few years behind them who can work independently, make trade-offs and help newer teammates.
How to prepare your application
- Check your résumé against the listed skills (Search) and give each one a concrete example: what you built or achieved, not just that you used it.
- Prepare examples of decisions you drove, how you measured the result, and how you worked with other teams.
- Be ready to discuss trade-offs and scope, not only the tools you have used.
- Read what Perplexity publishes about its product or team, so you can say clearly why this role and this company.
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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