PropTech Founder

AI Engineer Code Interview (Interviewer Perspective)

The video walks hiring managers through a practical AI‑engineer coding interview, using a GitHub repo that candidates clone, set up a Python virtual environment, and run a simple property‑search API. The test is open‑book, allowing documentation, Google, and LLMs, but forbids automated code‑completion tools, emphasizing problem‑solving and communication skills. Interviewers expect candidates to locate two deliberately hidden bugs: a sign error in the price‑boost calculation and an off‑by‑one mistake when iterating search results. Successful candidates print intermediate scores, reason through the math, and correct the operator and index start point rather than rewriting complex functions. Throughout, interviewers encourage loud thinking and clarifying questions to assess collaboration. Key moments include the interviewer’s observation that most candidates over‑engineer solutions, missing simple fixes like changing ‘‑’ to ‘+’ or starting loops at zero. He also highlights the importance of using print statements for debugging and leveraging LLMs for quick reference. The final task asks candidates to propose filtering by square footage and natural‑language features, prompting discussion of NLP or LLM‑based approaches. The exercise demonstrates that hiring AI engineers hinges less on exotic algorithms and more on clear communication, debugging discipline, and the ability to spot straightforward fixes. Candidates who practice these scenarios improve their interview performance and increase their chances of landing roles at top tech and finance firms.