Vetting deep tech candidates is incredibly difficult... especially in fields like Machine Learning and Quantum Computing where the academic credentials can look extremely intimidating.
It is easy to get blinded by a resume that lists a Ph.D. from Stanford, three papers in Nature, and a long list of complex physics terms.
But after ten years in the engineering recruitment game, I have learned the hard way that you cannot confuse academic brilliance with practical software engineering competence.
Here is what you actually need to look for when vetting builders for your startup.
First, check their code repositories... not just their list of papers. Look at how they structure their projects, handle dependency management, write test coverage, and document their APIs. Real builders write maintainable code... researchers often write single-file scripts that only run on their local machine.
Second, evaluate their understanding of standard software engineering tools. If a candidate knows everything about quantum algorithms but has never used Git, struggles with basic package managers, or doesn't know how to run a containerized environment... they are going to struggle in a startup environment.
Third, look for practical problem-solving capability. Instead of giving them textbook algorithm puzzles, ask them to build or optimize a simplified version of a real system they would work on... like optimizing a tensor pipeline or writing a mock compiler script.
Your startup needs people who can ship production-ready software... make sure your vetting process evaluates actual engineering competence, not just credentials.
Top comments (1)
This is a strong framing of a problem most deep-tech teams underestimate—vetting competence beyond credentials. In fields like AI, quantum, or systems engineering, degrees and buzzwords often don’t translate to real ability to design, debug, and ship working systems. I especially agree with the emphasis on evaluating thinking patterns rather than just outputs, since true engineering skill shows up in how someone approaches constraints, tradeoffs, and failure modes. One area worth expanding is structured practical assessments that simulate real-world ambiguity instead of leetcode-style filtering. Overall, a useful reminder that hiring for deep tech is fundamentally an evaluation of reasoning, not résumé signals.