Member of Technical Staff (Machine Learning Research Engineer)
Berlin · Berlin, Germany
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Search — team source
Department: Search
From the employer’s posting
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
Will you now or in the future require sponsorship for employment visa status in the United Kingdom, Germany or Serbia?
From the employer’s application form
What you’ll work on
Full postingDesign, train, and optimize large-scale deep learning models for retrieval and ranking
Architect and build core components of the search platform and model stack
Conduct research in representation learning, including contrastive and multimodal modeling
Deploy models, from boosting algorithms to LLMs, in a scalable and performant way
Build and optimize RAG pipelines for grounding and answer generation
From the employer’s posting
Design, train, and optimize large-scale deep learning models using frameworks like PyTorch
Architect and build core components of the search platform and model stack
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
What you’ll bring
All qualificationsCore experience
- Deep 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
- Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications
Qualification wording
Deep 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
Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications
Tools in this posting
- PyTorch
Source — Tool mentions in context
- 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
- 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
Posting record
Report an errorOpen 4 monthsWe first saw this posting 149 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026 · 149 days ago
- Recorded sightings
- 111
- Last seen by us
- Oct 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
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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
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Berlin, Germany
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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