I have an onsite Interview for an ML eng position next week. Does anyone have prep resources they would recommend? #machinelearning #interview tc: 280k
Leaving Pinterest ? What was your role and what was it like working there? To answer your question, my ML friends tell me it depends on the company and the role Some of them got really technical like you need a PhD Some of them was just creating the infra and very high level
Not yet but looking too ha. Just want smaller team and to work on challenging, interesting projects tbh
I would get familiar with regularization (L1/L2) and L1+ L2. Data collinearity, logistic regression / Linear regression maths and error assumptions . AUC/Precision Recall. Boosting/ Bagging (GBDT vs RF). RF feature importance / split. Bias variance trade offs . Naive bayes assumptions / derivations. Metrics relevant to your current work. I will also look at SGD optimizations especially Nostrov / ADAM and comparison with vanilla implementation. SGD in general ... general approaches for data preparation/ modeling and trade offs for pushing to production. Model evaluation. Recommendation/Ranking design.SVD/PCA/MF
Cool familiar with all of these but will brush up! Thanks :)
Do you have any experience in ML? Might be hard to recommend resources if not, as ML isn't something you learn overnight...
I have experience in building, evaluation and deploying ML models but don’t come from a traditional CS background, which HM knows. Mainly want to get a sense of what questions people have encountered during these interviews - eg linear alg, leetcode, etc. I expect to get more info on the panel today too.
Ah ok - I'm not an ML eng but at my previous company, ML candidates were definitely asked both modeling questions and leetcode challenges. Shouldn't be too hard for you to prepare for either 👍