About us

Parallel is a web infrastructure company. Our products are used by leading businesses in sales, marketing, insurance, and coding to build best-in-class AI agents with flexible and powerful programmatic access to the web.

We've raised $230 million from Kleiner Perkins, Sequoia, Index Ventures, Spark Capital, Khosla Ventures, First Round, and Terrain to build the web for AIs. We're currently valued at $2 billion and we're forming a world-class team of engineers, designers, marketers, sellers, researchers, and operational experts to achieve our mission.

About you

You're a researcher who thinks like an engineer, or an engineer who thinks like a researcher. You've worked on information retrieval systems, embedding models, or neural ranking at scale, or you're deeply curious about the fundamental problems that emerge when training models to understand and serve billions of web documents. You thrive in the space between theory and production, where elegant solutions must also run efficiently on real infrastructure. You're comfortable reading papers from SIGIR and RecSys one day and debugging distributed training pipelines the next.

The role

You'll design and train the models that power Parallel's APIs: the intelligence layer that helps A