The standard narrative about tech talent in Europe focuses on the London-Berlin-Stockholm axis, with supporting roles for Amsterdam, Paris, and occasionally Munich. This narrative was accurate for the previous decade and is now materially incomplete. What has changed is the combination of remote work normalisation, the maturation of technical universities outside the traditional cluster cities, and the exit of a cohort of senior engineers from the first generation of European tech unicorns who are now available to join early-stage companies — or starting their own. The DACH talent market in particular has shifted in ways that create specific opportunities for founders who are paying attention.
The most significant change in Berlin specifically is the pool of ML engineers who have cycled out of the research-to-industry pipeline. A generation of PhD graduates from TU Berlin, FU Berlin, and the Max Planck Institute network spent four to six years building applied ML systems in industrial and academic contexts, and many of them are now at career transition points — either having completed their first industry role and looking for a more mission-driven environment, or having been caught up in the post-2022 hiring contractions at large tech companies. This is not a soft labour market signal — it is a structural increase in the availability of genuinely senior ML talent for the kind of deep technical AI-native product work that characterises the companies we back. The signal is visible in the CV quality we see for technical roles at seed-stage portfolio companies in 2023 compared to 2021.
The Austrian and Swiss markets deserve more attention than they receive in the DACH talent narrative. Vienna has developed a specific density of enterprise software engineering talent in legal tech, financial services automation, and B2B marketplaces — partly as a consequence of the Central European professional services hub that the city has historically been. Zurich's talent market is expensive but technically exceptional, with particular strength in ML infrastructure, quantitative methods, and financial technology that reflects the profile of the major institutions headquartered there. For companies willing to manage a distributed engineering team across DACH rather than concentrating entirely in Berlin, the talent pool is meaningfully broader and often more experienced in enterprise technical contexts than a Berlin-only hiring strategy would access.
We are not saying the DACH talent market has become easy. It has not. Senior ML engineers with both research depth and production engineering experience remain scarce and expensive everywhere in the ecosystem. The competition for experienced commercial talent — specifically people with enterprise SaaS sales experience who understand the German procurement dynamic — remains intense. The companies that are building well-composed teams in this environment are typically those that have a specific and credible story about why their technical problem is interesting to someone with options, and those that have built their hiring process to move quickly when they find the right person rather than running a three-month evaluation cycle that loses candidates to competitors mid-process.
The pattern we find most encouraging is the emergence of founding teams that combine different components of the DACH talent stack: a technical co-founder with a research background from the Munich or Berlin university network, a commercial co-founder who spent four to six years inside a German enterprise as a practitioner in the function being automated, and an engineering hire who came out of the talent contraction at a large tech company and brings production systems experience. This combination — research-level ML understanding, domain-level process knowledge, and production engineering discipline — is exactly the team profile that can build AI-native workflow automation at the quality threshold German enterprise buyers require. The fact that this profile is increasingly available in the DACH market is a material input to our confidence in the thesis.