What is overfitting or underfitting? Which models are most likely to experience this, and why?

"I interviewed for a Senior Staff MLE role, and the biggest thing I’d say is Reddit felt pretty team dependent, so I wouldn’t assume every team runs the exact same loop. Mine started with a recruiter screen that surprisingly included a couple very basic ML questions, then a hands-on technical screen where I had to work with data in a notebook-style environment and get a model actually running. The final loop was ML fundamentals, ML system design, coding, and a hiring manager round. The ML system design round was the most interesting because I had prepped for a modeling-heavy conversation and got pushed much more into ML infra, like deployment, latency, feature pipelines, and how the A/B test would work under the hood."