Company / Careers / Engineering
ML Engineer
You design, train, and ship the models that turn billions of crawled pages into company data: built on terabytes, running at millions of tokens a second, answering the same every time they're asked. Every datapoint we sell passes through a model like yours.
Full disclosure
The way we work is not for everyone, and that's fine. We'd rather you know now than three interviews in. We dislike every side of micromanagement, so no one will hand you a detailed plan. You'll own things before you feel ready and the pace doesn't ease up.
If this sounds frustrating, trust that feeling. If it sounds like your kind of place, let's talk.
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 135M+ 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
- Invent the algorithms that pull signal out of very large, very messy datasets, knowing that what your models extract is what our graph knows.
- Build the training datasets your models learn from, because at billions of pages quality is decided there before any architecture choice.
- Train, evaluate, and deploy in massively parallel environments, owning each model end to end from data to production monitoring.
- Read performance off real production results, where 100M predictions a day give you feedback loops no benchmark can match.
- Put open-source LLMs and agentic workflows to work in real pipelines, so you iterate at the pace the web changes.
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
- You've shipped models to production and owned them after launch: monitoring, debugging, and fixing what breaks.
- Deep ML fundamentals: how to structure the data, choose the architecture, and reason about why a model behaves the way it does.
- Math and statistics solid enough to tell whether a result is genuinely wrong or just surprising.
- Fluent Python, and the appetite to master the Big Data stack the models run on: Spark, Flink, Cassandra, HDFS.
- The judgment to spend intelligence where it counts: when a problem needs a model, when a filter will do, and where your system will be confidently wrong.
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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