Perplexity

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

Internship Program Berlin Internship program: 12 - 24 weeks, full-time, in-person in the Berlin office. Responsibilities - Relentlessly push search quality forward — through models, data, tools, or any other leverage available. - 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 research in representation learning, including contrastive learning, multilingual, evaluation, and multimodal modeling for search and retrieval. - Build and optimize RAG pipelines for grounding and answer generation. Qualifications - 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. - Interested in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization and robust evaluation. - Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).