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.
About the job
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 Retrieval, Embedding Models, Neural Ranking, Deep Learning, Distributed Training, LLMs, Semantic Search
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