Machine Learning Research Engineer

Summary

Join Perplexity, a rapidly growing AI-powered answer engine, as a Senior Machine Learning Engineer. You will play a key role in building the next generation of advanced search technologies, focusing on retrieval and ranking. Responsibilities include pushing search quality forward, architecting and building core search platform components, designing and optimizing large-scale deep learning models, conducting advanced research in representation learning, deploying models, building RAG pipelines, and collaborating with various teams. The ideal candidate possesses a deep understanding of search and retrieval systems, a proven track record with large-scale systems, strong PyTorch proficiency, expertise in representation learning, and a strong publication record. A minimum of 3 years (preferably 5+) of relevant experience is required.

Requirements

• 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

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