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
You will own the multi-stage ranking pipeline that narrows a web-scale set of candidates down to the handful of best passages for a query, spending the least compute necessary to do it well. You will push precision and recall across fast candidate retrieval, lightweight first-pass ranking, and heavier neural reranking, decide how much compute each query deserves, and grade everything on real usage outcomes rather than offline proxies.
What they're looking for
- Bachelor's degree or equivalent combination of education, training, and professional experience
- A field relevant to the role as demonstrated through coursework, training, or professional experience
- Years of experience required will correlated with the internal job level requirements for this position
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.
You will own the multi-stage ranking pipeline that narrows a web-scale set of candidates down to the handful of best passages for a query, spending the least compute necessary to do it well. You will push precision and recall across fast candidate retrieval, lightweight first-pass ranking, and heavier neural reranking, decide how much compute each query deserves, and grade everything on real usage outcomes rather than offline proxies.
Have deep intuition for relevance, feature engineering, and the trade-offs between quality, latency, and cost. Think rigorously about how ranking, retrieval, and agent behavior inform one another, and...
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