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ParallelParallelPalo Alto, CA

Early Career Research Engineer

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.

150k – 300k/yr
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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