Startups · AI

Member of Technical Staff, Data Engineering

Parallel Web Systems · San Francisco or Palo Alto · On-site

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About Parallel Web Systems

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 build the data foundations that powers model training, product building and analytics across the company. You will work across the full lifecycle: designing pipelines that ingest and transform data at scale, building the storage and serving layers that make it fast and reliable to query, and establishing the quality, lineage, and observability systems that let others trust the data.

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 build the data foundations that powers model training, product building and analytics across the company. You will work across the full lifecycle: designing pipelines that ingest and transform data at scale, building the storage and serving layers that make it fast and reliable to query, and establishing the quality, lineage, and observability systems that let others trust the data. You will anticipate scaling bottlenecks before they appear and make the architectural calls that keep the platform ahead of growing demand.

Have deep intuition on distributed data processing, data modeling, and system reliability. You like to...

Read the full posting on Parallel Web Systems's site ↗

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