How can I successfully transition my career into the data and ML side, and ensure long-term relevance in my chosen field?
I'm a 30-year-old SDE-2 looking to shift my career towards data and machine learning. I’ve started as an Android Engineer and also have experience with React and Node.js. However, I no longer find these areas satisfying. Now, I understand the importance of staying consistent within a domain. For the next decade, I want to focus on a technology that will remain relevant for the next twenty years, without being affected by frequent framework changes.
I've noticed that people around 25 years old are able to build impressive projects thanks to good courses and tutorials. Although they make mistakes, it seems likely that within the next two or three years, they will have overcome these challenges. I find data and machine learning more rewarding because they involve answering meaningful questions. Recently, there was an internal opening in my company for a data and ML role, but I didn’t apply because I clearly lacked the necessary skills. However, I believe it’s time to switch and commit to this new direction. Any advice on making this transition smoothly and choosing the right path would be greatly appreciated.
Discussion
(3 comments)- 2
5 months agoFounding ML Engineer @ Lancey (YC S22)My goto advice when it comes to switching to ML is to find SWE roles in ML teams. There is a surprising amount of SWE work needed to be done within ML teams just for the deployment of a model. And no I'm not talking about MLOps.
For e.g. you need someone to get data from the user to your server, then you need to process the inputs for the ML model, it needs to be deployed and scaled, then after inferencing you need to surface back the predictions to the user.
You also need to do testing for all this. When you run it at scale even this inferencing can be very complex to handle such large volume of data and there are also constraints with needing real time or batching predictions. You get the idea
I don't suggest pre-learning tons of ML and Data Science. I go into depth on this here: https://www.jointaro.com/question/48WePYRfWEXgc5itTYUY/new-grad-backend-greater-mle/
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5 months agoTech Lead @ Robinhood, Meta, Course HeroSai also has a bunch of awesome advice about going deeper into MLE here: "How to build depth as a machine learning engineer?"
- 05 months agoTech Lead @ Robinhood, Meta, Course Hero
As Sai mentioned, it's way better to transition internally. Pivoting technical domains through a job switch is borderline impossible, especially for MLE and especially in this economy. You can learn more about how to make any engineering transition here: "How to transition from back-end development to distributed systems?"
The next time you see that internal opening, just apply! There's not much you can lose. If you are high-performer at your current company, there's a good chance you can get away with missing some requirements. In general, treat job "requirements" as more of a recommendation. I recommend talking to your manager about this as well.
