Web infrastructure for AI to search, extract, monitor, and reason over the world's information. Backed by Index, Khosla and Kleiner Perkins.
About the role
Our team works fully in-person , between our Palo Alto HQ and San Francisco office. We’re a flat, talent-dense organization dedicated to solving technical and creative problems. We seek like-minded individuals who share our passion for applying science, creativity, and consistency to big and complex problems with equally big outcomes. These are our values:
More about this role
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.
Job: As one of our Researchers - you will answer the question: how to train and scale a model that can serve a web index?
You: Have deep intuition on modern models and training. Like to argue how search, recommendations, and transformer models can converge. You care about your research being applied to product and systems that millions use.
Our team works fully in-person , between our Palo Alto HQ and San Francisco office. We’re a flat, talent-dense organization dedicated to solving technical and creative problems.
We seek like-minded individuals who share our passion for applying science, creativity, and consistency to big and...
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