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by rumdonut
1254 days ago
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Somewhat unrelated but a remark of yours was interesting to me. You mentioned PhDs sometimes have previous engineering experience; did you find it common for ML PhDs to have waited a few years before entering a program? I’m exploring doing the same but had thought the ship had sailed, now that I’m in industry. |
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Those candidates tend to be great as they are up on industry practices/technology like source control and databases where as some phds can have only academic coding experience. And they tend to have studied something they really knew they were interested in and really been the driver on their thesis project vs just contributing to their advisors research.
You also see great phds who didn't wait but really took ownership of their project, used source control, figured out distributed computing, contributed to open source, scraped/built their own datasets, understood the real world implications and hacked on side projects to develop coding skills.
And you see some who just completed a theoretical + computational project their advisor suggested on an existing dataset with the minimal amount of coding needed and little thought to implications/applications.