One tricky thing I've noticed when trying to hire ML people is that it's very difficult to tell when someone is okay at ML and when someone is great at ML from their past projects. Because machine learning is just statistics, it is often resilient to errors in design and implementation. You can mess things up and still get reasonable (but suboptimal) results.
This means that someone can easily claim "this problem is difficult to learn for machines" when they fail or claim "we got X% accuracy look how great we are!" when they do okay. But a really good engineer or scientist would have succeeded in the same task, or have gotten X+10% accuracy with the right models, data, or engineering.
On my resume, I compare my results to either the previously implemented model or the state-of-the-art for that specific problem. If you bring someone in for an interview, ask them what the previous best was.
This means that someone can easily claim "this problem is difficult to learn for machines" when they fail or claim "we got X% accuracy look how great we are!" when they do okay. But a really good engineer or scientist would have succeeded in the same task, or have gotten X+10% accuracy with the right models, data, or engineering.