AudaciousThe web infrastructure for AI agents.
Today’s web and search engines were built for humans. As AI agents increasingly search, research, monitor, and act on information from the web, they need infrastructure designed for machines. Parallel Web Systems is building that layer, spanning crawling, indexing, search ranking, retrieval, reasoning, and model training.
Parag Agrawal
CTO and then CEO of Twitter, where he spent over a decade on internet-scale systems. PhD in Computer Science from Stanford, with a background in distributed systems and ML.
Travers Nisbet
Previously led bank-partnership strategy and ran the savings product at Chime. Before that, McKinsey in San Francisco, working on payments and growth strategy for technology marketplaces.
Utkarsh Srivastava
Previously SVP of Engineering at Patreon, led Android messaging, video, and calling at Google, and was Senior Director of Engineering at Twitter. Co-created Apache Pig at Yahoo Research. PhD in Computer Science from Stanford.
Parag Agrawal on building a new web for AI agents.
Foundational infrastructure, not another application on top of it.
You would be building the technology other AI companies build on top of. Parallel is often invisible to the end user: its infrastructure sits underneath other AI products and agents, across legal, finance, sales, insurance, and research.
The web was built for humans
Search engines assume a person typing a query and reading a page. Agents do neither, and the infrastructure underneath has never been redesigned for them.
A genuinely new technical problem
Agents interact with the web very differently from people, potentially consuming thousands of documents or searching continuously in the background. Existing infrastructure was never designed for that.
Improve retrieval, improve everything
Better retrieval, ranking, or indexing at Parallel means many downstream AI products get better with it. Early, but validated: a small, talent-dense team with real adoption, capital, and sophisticated customers.
Already underneath the products you know.
Over 100,000 developers are building with Parallel, alongside insurers, banks, and hedge funds.
100K+ developers
Building on Parallel’s crawling, indexing, search, and retrieval infrastructure.
Named and unnamed
Harvey, Notion, Profound, Opendoor, and Actively, plus insurers, banks, and hedge funds.
Legal, finance, sales, insurance, research
Parallel is often invisible to the end user, sitting underneath other AI products and agents.
Backed by top-tier investors.
$230M raised at a $2B valuation. The latest round was a $100M Series B led by Sequoia in April 2026, in which every existing investor increased participation.
Recent coverage.
What they’re hiring for.
21 open roles in all, 9 of them in engineering. Small and flat (~40 engineers), talent-dense, and AI-first.
- Retrieval & Indexing
- Search Ranking
- Model Training
- Data Engineering
- Backend / API
- Research
- Deployed Engineering
- Obsess over craft
- Accelerate change
- Make high-conviction bets
- ~40 engineers · small · flat · AI-first
- Palo Alto, at least 3 days a weekThe baseline expectation. If you’re based in SF, you can work from the SF office the other 2 days.
- New York works tooNew York-based candidates are eligible at all levels, including early career.
If you want to build what other AI companies build on, let’s talk.
Foundational infrastructure, a genuinely new technical problem, and a small team that has already operated systems at this scale.