Company / Careers / Engineering
Data Assets Intern
Some of what's true about companies never shows up on the open web: it lives in official registries, investor lists, datasets in every shape imaginable. You find those sources, make sense of them, and turn them into usable data. Each one you bring in is in production within days, sometimes hours, and the graph knows companies it didn't know yesterday.
Full disclosure
The way we work is not for everyone. Problems don't come broken down into bite-sized pieces: you'll get real ownership early, sometimes before you feel ready, and from there it's yours to run with. The pace is sustained, and with the company growing exponentially, it won't ease up any time soon.
Better to know in five minutes than to find out in month three. If reading this feels like a no, trust that feeling. If it raises flags, bring them up early, and we'll give you straight answers. If autonomy and high stakes are what you're after, you may have just found the right place to grow.
Who we are
- A deeptech startup of 60 people in Bucharest, building the machine that reads the company world.
- Our customers include the giants of the data world, the same companies you'd expect to be our competitors.
- Founded in 2019, VC-backed. The team stays small on purpose, so decisions stay with the person doing the work.
- Most of the team is technical, founders included. The decisions that shape the company are made by people who understand the low-level work behind it.
- What we run in-house (from our crawlers to our own LLMs) is what several entire companies get built around. Here it's only one team.
Who you are
- You own your work like a founder owns a product.
- A growth mindset, able to capitalize on unprecedented contexts through your skills and abilities.
- A strong problem solver, visible in the way you deal with the tension between brief and shipping.
- Resilient, especially in front of failure, the kind that always comes paired with pioneering work.
- An appetite to grapple with a variety of technical challenges.
What we do
- Crawl the entire internet and decide, page by page, what matters.
- Interpret everything we find with LLMs we train ourselves, in 125 languages.
- Resolve every source under the company it belongs to, in one living graph of 186M+ companies.
- Trace every datapoint back to the source it came from, so every judgment can be defended.
- Keep the history, so we notice when any one of them changes.
What you'll do
- Stay on the lookout for new datasets (registries, investor data, business data in any form) and judge which ones are worth the effort.
- Make sense of each source, normalize it, and integrate it: every dataset arrives in a different shape, and yours is the judgment that turns it into usable data.
- Monitor the pipelines you've built and fix them when they break: whenever a format changes, or the quality drifts and only someone looking at the data would notice.
- Pick the fastest honest path to get data in: crawl the source directly, put our AI tooling on it, or build the missing piece yourself.
- See each dataset through to the end: in production, checked, and usable by the rest of the company.
What you'll find here
- Founders who built products serving billions of users, across web, big data, and cloud.
- Infrastructure most companies rent, built and run in-house: crawlers, GPUs, custom LLMs.
- Scale you'd otherwise wait a career to touch: 4.2B pages a month, 3M tokens a second, from day one.
- Problems no one has solved before, some so far out that academic papers are the only formal guide we have.
- Five disciplines under one roof (crawling, ML, big data, infrastructure, product) close enough that you see how all of them actually work.
What we look for
- Basic coding skills and SQL, you'll pick up everything else here.
- The instinct to actually look at the data before trusting it: a shifted format, a column that's suddenly empty, a number that can't be right.
- Comfort with sources that are messy, undocumented, and different every single time.
- Resourcefulness: when the obvious way in doesn't work, you find another one.
- Ease working with AI tools as a normal part of how you work, and the judgment to know when and where to double-check them.
What we offer
- Fair, market-aligned base, ESOP tied to impact (for permanent roles), and a high salary growth rate tied to performance.
- Decisions that would need three approvals elsewhere are yours to make, and yours to answer for.
- Growth at the pace you can take, and responsibility that expands as fast as you prove you can hold it.
- What you build lands in front of some of the largest companies in the world, often within weeks.
- Steep learning curve, no matter how experienced you are, with people who've climbed it a desk away when you're stuck.
What we expect in return
- High tolerance for ambiguity, marked by your ambition to push forward with incomplete information.
- High speed and uncompromising quality in your work.
- The ability to quickly and effectively evaluate technical tradeoffs and translate them into relevant scenarios
- Genuine aversion to any customer or colleague struggling with something you delivered.
- Ask what would make it ten times better, starting with your own work.
How we hire
A process built to help both of us decide.
We keep it informal because that's how we actually work. And we take it seriously because one hire can change a company this size. The way we communicate throughout is designed so that either of us can openly say it isn't working, even mid-interview.
- 1Home AssignmentA hands-on problem cut from the real work of this role. You'll know what the job is like before you say yes to it, and so will we.
- 2Team interviewsSessions with the people you would actually work beside, one of which is face-to-face and includes live tasks.
- 3DecisionA yes or a no, typically fast. We do not ghost.
Careers
Recognize yourself in all of this?
Send your application and a note on why this role fits. We read every one, and if it matches what we're looking for, you'll hear from us.
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