You interviewed for an AI infrastructure / LLM serving internship role and were told the rejection reason was insufficient familiarity with vLLM, including needing to understand its core mechanisms, read source code, and ideally contribute.
Question:
-
What
foundational skills
should an AI Infra intern candidate have so a team believes they can ramp up quickly?
-
What are the
core concepts/mechanisms
in an LLM inference engine (e.g., vLLM-style) that you should be able to explain?
-
What concrete
projects or contributions
can you do to demonstrate readiness (including how to approach reading and contributing to a large open-source codebase)?