Q is - should I try to break into SWE or MLE? I finished my MS, specializing in AI and Big data - 4 years ago. Since then, the domain that I ve been working in is called - Data Eng/ Data platform eng/ Data application eng etc. Basically, I build scala/java big data pipelines using spark/kafka/streaming frameworks. I believe I should pivot, beacuse - A lot of domain knowledge/techniques are now automated in recent big data frameworks. - In most cases, big-data optimizations are not deal breakers. - After a certain point, the only "growth" left would be learning about the company business. I m more interested in engineering than business. - SWE/MLE pay is much better. I have decent exposure to both domains, although only academically. Some intense courses online should bridge the gap I believe. I plan to use this advise seriously. Thanks! YOE 5 TC 200K $, SF Bay area. #SWE #DataEngineering #MLE
OP, I am in a similar situation. What did you end up with?
Some interview qs I've gotten for MLE interviews How would you identify similar topics using topic modeling on unstructured data? How would you identify sentiment, for example, the bottle cap on coke bottles using social data? How would you leverage a document db to provide similar space optimizations that a time series database provides? How would you use an LLC as a task orchestrator to dive deep using market trend analysis and financial insights as the input What is the activation function of yolo models (lol trivia from snap round) ML infra questions, feature stores, data warehouse/data lake Leetcode, Sys design If that sounds good for you, go for MLE, but make sure your job entails what you actually want to be doing and not a glorified data engineer or worse gpt prompt engineer