Could someone who got the job as a Machine Learning Engineer at Google, Facebook, Apple, Amazon guide me how to prepare for ML engineer roles at those companies?
Specifically, I am looking for these inputs:
1. How many LC questions should I do to feel confident and land a job in the above stated companies? Should I focus on LC medium and also hard?
2. Does Facebook/Apple/Amazon ask questions based on the list mentioned in LC with the company tag?
3. How much preparation is needed for system design?
4. Should I have deep understanding on ML algorithms including Deep neural nets?
Thanks
TC: 🥜
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comments
1. LC medium. Number depends on your comfort level. I had 300+ solved including FB top 100.
2. Yes. All their questions were from tagged LC questions.
3. Not much. For FB, understand generic recommender systems design process. Deep dive into areas like data preparation, storage, processing, ML algo, candidate generation, filtering, ranking, deployment at scale. Expect to be asked any question in this pipeline.
4. No, according to my experience. If you have put any relevant experience in your resume you might get asked a few questions during behavioral round, but FAANG type companies mostly care about their interview bar.
Prepare well in generic system design too since there’ll be a round for that.
1.no idea
2. No idea
3. If you are already in industry and have worked with systems at scale, there shouldn’t be anything terribly new here.
4. Roughly, yes. It appears today that interviewers focus effectively exclusively on neural nets which I find strange 🤷🏻♂️ many of the underlying concepts generalize though, so having a deep understanding is extremely useful.
All that said, my opinion is that these designations (machine learning engineer, research scientist, software engineer, etc) exist for the sake of it. The job profiles are pretty similar and depending on the team, you can get hired for many different skill sets; solid breadth, deep knowledge in a single domain, deep infra knowledge. The last one is the rarest, as best I can determine.
But there are many ml specific things too. Knowledge of target domain for the model: on device, android, compute monitoring, serving stack, user feedback stack.