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Member of Technical Staff, Back-end & API

Parallel Web Systems
Posted onFeb 16, 2026
LocationSan Francisco, California, United States | Palo Alto, California, United States (On-site)
Employment typeFull-time

At Parallel Web Systems, we are bringing a new web to life: it’s built with, by, and for AIs. Our work spans innovations across crawling, indexing, ranking, retrieval, and reasoning systems. Our first product is a set of APIs for AIs to do more with web data.

We are a fully in-person team based in both San Francisco and Palo Alto, CA. Our organization is flat; our team is small and talent dense.

We want to talk to you if you are someone who can bring us closer to living our aspirational values:

  1. Own customer impact -It’s on us to ensure real-world outcomes for our customers.

  2. Obsess over craft -Perfect every detail because quality compounds.

  3. Accelerate change -Ship fast, adapt faster, and move frontier ideas into production.

  4. Create win-wins -Creatively turn trade-offs into upside.

  5. Make high-conviction bets -Try and fail. But succeed an unfair amount.

Job: You will build and maintain our backend that already handles millions of custom requests daily, and ensure that it’s reliable, performant and exceeds expectations for our customers as we scale. You will also be a steward of our API and ensure that it’s developer-friendly and easy to integrate with.

You: Have deep intuition on distributed systems, databases, maintainable code design, and API design. You like to reason about trade-offs between speed, scalability, and developer ergonomics. You care about your work powering products and systems that millions rely on, and about APIs that developers love to use.

Our founder is Parag Agrawal. Previously, he was the CEO and CTO at Twitter. Our investors include First Round Capital, Index Ventures, Khosla Ventures, and many others.

Parallel Web Systems

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Parallel Web Systems builds web search and research APIs purpose-built for AI agents, providing production-grade infrastructure that outperforms humans and leading AI models on complex research benchmarks.

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