I had a lyft onsite for SWE ML last week. Here's how it went. 1. 90 mins round. A Leetcode medium to be coded and submit the code via email immediately to the interviewer at the end of 90 mins. I could code it up, explain the solution and ran the attached test cases. Went well. [I forgot to handle edge cases I think] 2. leetcode medium. Went well. Interviewer seemed to like my solution. We had a good conversation. 3. Hiring Manager. Behavioural. Went well. 4. ML System Design: I was asked to formulate a ranking problem using ML. I was able to come up with different features I would use. Got a bit stuck in formulating labels. And evaluation method but was able to tell after some hints. We also talked about embeddings to be used as festures and that lead to DL. But I said that I have not done DL so not comfortable talking about it. I was a bit skeptical of the last round. Other than that all went well. Today got a reject email. I would like to know from folks where I might have gone wrong? I could really use the ML sys des feedback for future interviews. PS: I have an offer from Amazon Alexa Speech Team in Bay Area. Would appreciate any suggestions for this too. SDE2 L5 TC: 255k yoe - 3 #pinterest #faang #swe #machinelearning
That seems like a low SDE2 offer for Bay Area.
Ichecked on levels.fyi. Seemed reasonable to me. My main goal is to do good ML work and get out of Evernote ASAP because of toxic culture
Do you mind sharing the breakdown?
Sorry to hear about the rejection. The ML sys design questions are very subjective and I imagine it's very hard to say exactly what they have in mind unless you have worked on exactly the same problem before
I feel when I said that I havenot worked on deep learning for the embedding discussion, that might have been a red flag. I have a confidence issue when it comes to talking about DL because I have not worked on it.
I totally get what you mean, it can be intimidating to talk about DL if you have not worked on it that much. it's a chicken and egg problem because you need experience with it to get experience with it. I would suggest not to take the rejection to heart, but instead really think if the role aligns with your interests and passion. If ML SWE is indeed what you are passionate about and want to grow in, you should probably start taking DL courses and actually trying out TF Pytorch etc on some simple datasets like in Kaggle to get the hang of it. Recruiters or managers will appreciate if you say that in your spare time you tried this and that project, even better if you create some simple GitHub page with your experiments. Only by trying it out and seeing where it works and where it falls can you confidently talk about it and how it can be used to solve real life problems
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What happens when most of your team is Indian?
Yoe?
3 yoe
Did you try to negotiate or was this the final offer after negotiations?