Search Machine Learning Research Engineer (Berlin)
Role Overview
As a Senior Machine Learning Research Engineer at Perplexity, you will focus on advancing search technologies, specifically in retrieval and ranking. Your daily tasks will include architecting core components, designing and optimizing large-scale deep learning models, and conducting research in representation learning. You will work collaboratively with cross-functional teams, significantly impacting the quality and performance of search solutions.
Perks & Benefits
This remote position offers a flexible work environment, allowing you to work from anywhere. While specific time zone expectations are not outlined, typical tech job flexibility is implied. Perplexity fosters a culture of innovation and ownership, providing opportunities for career growth through cutting-edge projects and collaboration with top talents in AI/ML.
Full Job Description
Perplexity is seeking an experienced Senior Machine Learning 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
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