Member of Technical Staff (Machine Learning Research Engineer)
Role Overview
As a Machine Learning Research Engineer at Perplexity, you will focus on developing advanced search technologies, particularly in retrieval and ranking. This senior role involves designing and optimizing large-scale deep learning models, conducting research in representation learning, and collaborating with cross-functional teams to enhance search quality and performance.
Perks & Benefits
This remote position offers a flexible work environment with opportunities for career growth in a dynamic team. While specific perks are not detailed, candidates can expect a culture that values ownership and execution, along with collaboration across Data, AI, Infrastructure, and Product teams. The role likely supports work across various time zones, accommodating a global workforce.
Full 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
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