Member of Technical Staff (Machine Learning Research Engineer)
At a glance
Senior AI Research role at Perplexity. Berlin · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Improve search relevance by developing models, refining data, and building tools that lift retrieval and ranking quality
- Build central search-platform and model-stack components, then train and tune large deep-learning models
- Research contrastive, multilingual, and multimodal representation methods for search and retrieval
- Put models ranging from boosting methods to LLMs into scalable, responsive production use
- Develop RAG workflows for grounded answers and coordinate delivery with data, AI, infrastructure, and product teams
What you bring
- At least three years in search, recommender systems, or closely related research, with five or more preferred
- Strong PyTorch skills, including distributed model training and performance tuning for large models
- Deep knowledge of retrieval quality measurement and experience with large-scale search or recommendation systems
- Expertise in contrastive representation learning and aligning embeddings across multilingual or multimodal tasks
- A substantial AI/ML publication record at conferences or workshops such as NeurIPS, ICML, or another listed venue
Who this fits
This senior role suits a research engineer who takes initiative and can carry work through to delivery. The position is based in Berlin, Germany.
From the employer
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