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Perplexity AI is hiring a Machine Learning Research Engineer to advance its AI-powered search platform, with a focus on retrieval and ranking systems. You will drive search quality improvements through model development, data optimization, and infrastructure innovation.
Key responsibilities include architecting and building core search platform components; designing, training, and optimizing large-scale deep learning models using PyTorch with distributed training techniques (PyTorch Distributed, DeepSpeed, FSDP); conducting advanced research in representation learning, contrastive learning, multilingual and multimodal modeling for search; deploying models from boosting algorithms to LLMs at scale; building and optimizing RAG pipelines for grounding and answer generation; and collaborating cross-functionally with Data, AI, Infrastructure, and Product teams.
This is a high-impact role for someone passionate about search technology and willing to leverage any available lever—models, data, tools, or novel approaches—to push quality forward.
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 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 (NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR)
- Self-driven with strong ownership and execution mindset
- Minimum 3 years (preferably 5+) working on search, recommender systems, or closely related research areas
About Perplexity AI
AI / Data / Infrastructure — AI answer engine and search product for consumers and enterprises.
