Designs and trains embedding and retrieval models for AI agents to access web data at hyperscale, balancing research innovation with production efficiency for sub-second latency and fresh indexes.
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On-siteEntry levelML Engineering
About the role
Responsibilities
Design and train models powering Parallel's APIs for AI agents to find information from the open web.
Tackle research problems at hyperscale: train embedding models capturing semantic intent across diverse query types.
Balance model expressiveness with sub-second retrieval latency.
Maintain index freshness for constantly updating web content without full rebuilds.
Build information retrieval systems for AI agents with complex, multi-hop queries using classical IR techniques and modern deep learning.
Requirements
Experience with information retrieval systems, embedding models, or neural ranking at scale.
Ability to work between theory and production, reading SIGIR/RecSys papers and debugging distributed training pipelines.
Thrive in solving fundamental problems for training models on billions of web documents.
Compensation & Benefits
Competitive salary
Generous equity
Visa sponsorships
401K plans
Daily lunch & office snacks
Dinner at the office
Unlimited vacation
Caltrain pass reimbursement
Skills
Information RetrievalEmbedding ModelsNeural RankingDeep LearningDistributed TrainingLLMsSemantic Search
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