Early Career Research Engineer

Sourced from feeny.ai

Parallel Web Systems is building web infrastructure for AI agents, and this role designs and trains the retrieval and embedding models behind their APIs. You'll work at the intersection of classical information retrieval and modern deep learning, solving hyperscale problems like semantic embedding training, sub-second retrieval latency, and index freshness. The team works fully in-person between Palo Alto and San Francisco.

• Design and train embedding and neural ranking models powering Parallel's APIs
• Balance model expressiveness with sub-second retrieval latency
• Maintain index freshness as the web updates constantly
• Apply classical IR techniques alongside modern deep learning at scale
• Debug distributed training pipelines and translate research into production systems

• Bachelor's degree or equivalent combination of education, training, and professional experience
• Background in a field relevant to the role via coursework, training, or professional experience
• Interest in information retrieval, embedding models, or neural ranking at scale
• Comfort moving between research theory and production infrastructure

• Experience with information retrieval systems or embedding models at scale
• Familiarity with SIGIR and RecSys research
• Experience debugging distributed training pipelines