▸ The First 10 Engineering Hires: Patterns We Consistently See Across European Startups: The first engineering hires often shape far more than product output. They influence technical standards, future hiring decisions, team culture, and ultimately how quickly a company reaches product-market fit. While every startup follows a different path, we consistently observe similar hiring patterns emerging across European startups as they progress from Founding Engineer to Product Engineer, infrastructure specialists, AI talent, and eventually technical leadership. Understanding these patterns can help founders make better hiring decisions at every stage of growth.
Table of Contents
- The Short Answer
- Why the First 10 Hires Matter More Than the Next 100
- A Typical Startup Hiring Journey
- Patterns We See Across European Startups
- The Biggest Hiring Mistakes
- How Geography Changes the First 10 Hires
- There Is No Perfect Team Structure
- The Bottom Line
- Building your first engineering team?
The Short Answer
There is no perfect blueprint for building an engineering team. Every startup’s path looks slightly different depending on product, founder background, and funding.
But after working with start-ups across Europe on technical hiring, certain patterns appear repeatedly. The first ten engineering hires are rarely ten software engineers. The strongest early teams typically combine product thinking, engineering execution, infrastructure capability, and eventually technical leadership in different proportions depending on stage.
The goal at this stage is not to maximise headcount. It is to remove the biggest bottleneck the company has right now, then the next one, then the next.
This article describes patterns we observe, not a universal hiring plan to copy.
Why the First 10 Hires Matter More Than the Next 100
Early engineering hires shape far more than their own output. They set the technical standards the next fifty engineers will inherit. It will influence who gets hired next, through referrals, through the bar they implicitly set in interviews, through the kind of work culture they normalise. They shape product velocity at the exact moment when speed of learning determines whether the company survives to find product-market fit.
A mediocre hire at engineer 40 is a containable problem. A mediocre hire at engineer 2, particularly if they are influential in shaping engineering culture, can take 18 months to unwind, because by the time the problem is visible, their fingerprints are on the codebase, the hiring process, and the team’s expectations of what “normal” looks like.
This is why the first ten hires deserve disproportionate attention relative to their number. They are not 10% of a 100-person engineering org. They are the genetic material of it.
A Typical Startup Hiring Journey
| Stage | Typical Engineering Focus |
|---|---|
| Pre-Seed | Building an MVP — proving the idea works technically |
| Seed | Finding product-market fit — rapid iteration based on user feedback |
| Series A | Scaling product delivery — repeatable, faster shipping |
| Series B | Building organisational structure — specialisation, leadership, infrastructure maturity |
This is a pattern, not a rule. Some companies reach Series A with three engineers. Some have fifteen by Seed. The stage describes the problem the company is solving more than it describes a headcount target.
Hire #1: The Founding Engineer
The first engineering hire at most European startups we see is not a specialist. It is a generalist capable of operating with significant ambiguity — someone who can take a rough product idea and turn it into something real without a complete specification, a dedicated product manager, or an existing codebase to extend.
This person typically has high tolerance for unclear requirements, genuine product ownership instinct (they care whether the thing works for users, not just whether it compiles), and a startup mindset that treats constraints as a normal operating condition rather than a problem to escalate.
A pattern worth noting: many startups now choose a Founding Engineer before they choose a CTO. The CTO title implies management responsibility and external representation — board updates, investor technical due diligence, organisational design — that often is not needed yet at one or two engineers. The Founding Engineer role focuses purely on building. The CTO conversation tends to happen later, sometimes with the same person, sometimes not.
Hire #2: Another Strong Generalist
The second engineering hire we most commonly see is another generalist rather than the first specialist. At this stage, the company typically does not yet know which specific technical problems will turn out to be hardest — so hiring someone who can move fluidly between frontend, backend, and infrastructure work tends to provide more value than hiring deep expertise in one narrow area.
Full-stack capability matters here specifically because the team is small enough that handoffs between specialists create real friction. If hire #1 builds the backend and hire #2 can only do frontend, every feature requires coordination between two people for work that one flexible generalist could do alone, faster.
The pattern we observe: specialisation becomes valuable once the company knows what kind of technical problems it actually has. Before that point, flexibility outperforms depth.
Hires #3–#5: Product Engineers Start Appearing
Somewhere between the third and fifth engineering hire, we consistently see companies begin prioritising product-minded engineers specifically — not just generalists, but people who actively want ownership over the customer experience, not just the code that powers it.
This is the stage where customer-facing feature velocity becomes the primary bottleneck. The company has a working product. The question is no longer “can we build this” but “what should we build next, and how fast can we learn whether it worked.” Engineers who can shorten that loop — talking to users, forming opinions about what matters, shipping fast, and iterating based on real feedback — provide outsized value relative to engineers who wait for fully specified requirements.
This is also typically where the line between “Product Engineer” and “Software Engineer” becomes operationally meaningful for the first time. Earlier hires often did both kinds of work by necessity. At this stage, companies start deliberately hiring for the product-engineering orientation specifically.
Hires #5–#7: Infrastructure Starts to Matter
Somewhere in this range, a different kind of problem becomes visible. The product works, has real users, and the team starts noticing things that were not problems before: cloud costs creeping up without anyone tracking them, deployments that occasionally break things in ways nobody fully understands, and questions about data security that founders cannot confidently answer.
This is typically when companies introduce their first dedicated DevOps or platform engineering hire — sometimes the company’s first specialist hire of any kind. The role differs from the generalist engineers before it: this person is thinking about reliability, observability, deployment pipelines, and security posture rather than shipping user-facing features.
For AI-native companies specifically, this is often also when MLOps starts to matter — not because the company needs more AI capability, but because the AI features that earlier engineers shipped quickly are starting to show the operational fragility that comes from running in production at real scale, without monitoring or rollback infrastructure.
The pattern: infrastructure problems are invisible until they are urgent. Companies that wait until the urgency is undeniable tend to have a more painful first DevOps hire than companies that anticipate the need slightly earlier.
Hires #6–#8: AI Changes the Mix
This is one of the more structurally significant shifts we observe in 2026 compared to equivalent-stage companies three years ago. Many startups — including companies that are not AI-native by product category — are now hiring AI Engineers, Applied AI Engineers, or ML Engineers into this window instead of additional general software engineers.
The driver is usually product-specific: the company has identified a feature or workflow that AI can meaningfully improve, and the engineer who builds it needs different skills than the generalists hired earlier — comfort with LLM APIs, RAG architecture, prompt evaluation, or in some cases genuine ML model training.
A pattern worth flagging clearly: most companies at this stage need an Applied AI Engineer, not an ML Engineer. The confusion between these roles is one of the more common hiring missteps we see — companies write a job description assuming they need someone who trains models, when the actual need is someone who can integrate and productionise existing AI capability. Getting this distinction right at hire six or seven saves real time.
Hires #8–#10: Leadership Begins to Emerge
By the time a company is making its eighth, ninth, or tenth engineering hire, a different kind of gap typically becomes visible: there are now enough engineers that coordination, prioritisation, and technical direction-setting require more deliberate attention than they did when the team was three or four people working in the same room.
This is the window where we most commonly see the first Engineering Manager, Tech Lead, or formally appointed CTO emerge — and it is worth being explicit about the pattern: leadership in these companies usually appears after a team exists, not before it. Hiring a manager before there is a team to manage tends to create role ambiguity and frustration on both sides — the leader has no one to lead, and the company has paid leadership-level compensation for execution-level output.
The common exception is technical co-founders who held the CTO title from day one. In that scenario, leadership existed before the team — but functionally, even technical co-founders we observe usually spend their first ten hires building hands-on, with the formal leadership and management dimension of the role becoming activated only once the team reaches a size that requires it.
Patterns We See Across European Startups
A few observations that appear consistently — presented as patterns, not universal rules.
European startup engineering teams in 2026 are generally smaller at each funding stage than equivalent companies five years ago. The combination of AI development tools and capital discipline has shifted what “enough engineers” looks like at Seed and Series A.
Product ownership is showing up earlier in engineer job descriptions and earlier in actual hiring decisions. The Product Engineer orientation that used to emerge around hire 10–15 is increasingly present from hire 2 or 3.
AI tooling usage is now close to universal among the engineering teams we work with, regardless of whether the company’s product is AI-native. This affects output per engineer meaningfully enough that headcount planning models from 2021 consistently overestimate the team size needed to ship a given roadmap.
There is a stronger preference for generalists earlier and specialists later than we observed in previous years. Specialist hires — DevOps, MLOps, security — are appearing slightly later in the sequence as generalist engineers absorb more of that work for longer before it becomes a dedicated role.
Leaner early teams appear to be reaching revenue milestones faster relative to headcount, though this is a directional observation rather than a rigorously isolated causal claim — many variables affect time-to-revenue beyond team size.
The Biggest Hiring Mistakes
Hiring specialists too early. A dedicated security engineer at hire 3, before there is meaningful security surface area to protect, is premature. The need will become real — usually later than founders expect.
Hiring managers before builders. An Engineering Manager with no team to manage either invents work or becomes frustrated. Leadership hires should follow team formation, not precede it, in the large majority of cases we observe.
Ignoring infrastructure until it breaks. Companies that wait for a production incident to take DevOps seriously pay a steeper price — in downtime, in customer trust, and in the eventual hire’s onboarding difficulty — than companies that anticipate the need one or two hires earlier.
Hiring for titles rather than problems. A job description built around an impressive title (“Senior AI Engineer”) rather than a specific bottleneck (“we need someone who can integrate an LLM into our existing product within six weeks”) consistently produces worse hiring outcomes.
Building large teams before product-market fit. Headcount growth that outpaces validated learning is one of the most consistent patterns behind capital-inefficient early-stage companies. More engineers does not reliably produce more product-market fit; it often just produces more code shipped against an unvalidated hypothesis.
How Geography Changes the First 10 Hires
Talent availability shapes early hiring decisions in ways that founders do not always anticipate before they start a search.
Berlin and Amsterdam offer deep applied AI and product engineering talent pools — useful for hires 1 through 5 where generalist and product-minded profiles are the priority. Competition for the strongest candidates in both cities is real, and outreach needs to be specific.
Barcelona has become a genuinely strong market for early applied engineering hires at a cost point that extends runway meaningfully compared to Northern Europe — relevant for Seed-stage companies watching burn closely.
Warsaw is where many companies find their first DevOps or platform engineering hire once that need becomes real — deep specialist talent, B2B contractor culture that allows fast, flexible engagement, and full timezone overlap with Western Europe.
London and Paris offer the deepest pools for AI and ML specialist hires specifically — relevant once a company reaches the stage where AI Engineers or ML Engineers become the right addition rather than a generalist. Both markets are also the most competitive and highest-cost in Europe for these profiles.
The pattern: the geography of your first ten hires often ends up reflecting the geography of the specific problems those hires are solving, more than a single deliberate “where should we be based” decision made on day one.
Related: European Tech Hubs · European Engineering Talent Heat Map · Hire Engineers in Europe
There Is No Perfect Team Structure
Every startup we observe is solving a slightly different problem. Technical complexity varies enormously — a company building infrastructure-heavy AI tooling has a different hiring sequence than a company building a relatively simple SaaS product on top of existing APIs. Industry matters — regulated sectors require different early specialist hires than consumer products. Funding profile matters — a capital-efficient Seed round produces different hiring decisions than a large Series A. Founder technical background matters enormously — a strong technical co-founder changes what the first few hires need to cover.
The patterns in this article describe what we see commonly, not what should be copied mechanically. The goal at every stage is the same: identify the actual bottleneck the company has right now, and hire to solve that — not to match a template.
The Bottom Line
The strongest early engineering teams we observe across European startups are built gradually and deliberately, even when the underlying pace feels fast. Most begin with versatile builders rather than specialists. Specialisation tends to arrive later, once the specific technical problems are clear enough to justify it. Leadership usually emerges once there is a team to lead, not before.
The first ten hires shape far more than the next ten. They tend to shape the technical standards, hiring instincts, and product culture that the next hundred engineers will inherit — which is why the sequence, not just the headcount, deserves real attention.
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