How to Vet Deep Tech Talent Beyond Academic Credentials

Hiring for deep tech roles in fields like machine learning and quantum computing is challenging, as impressive academic credentials do not always translate to practical engineering ability. A seasoned engineering recruiter with ten years of experience advises startups to assess candidates' code repositories rather than relying solely on published research or degrees. Evaluators should look for clean project structure, test coverage, API documentation, and familiarity with standard tools such as Git and containerized environments. Practical problem-solving tasks — like optimizing a tensor pipeline or writing a mock compiler script — are recommended over abstract algorithm puzzles. The core advice is that startup hiring processes must measure real-world engineering competence, not just academic distinction.