Article URL: https://www.maxmynter.com/pages/blog/jobhunt Comments URL: https://news.ycombinator.com/item?id=49051707 Points: 18 # Comments: 5

Note: This is also published on Substack (where you can give me your mail if you want emails whenever I publish something). A couple of days ago, I signed as a research engineer with Mistral, one of the few ML foundation model labs with more than a billion-dollar funding. My excitement on Twitter found quite some resonance — partly in the form of questions for advice. Getting here was not an accident. I have strategically worked towards this outcome for an extended period, and I have a few things to share about what worked for me. In a sense, this blog post is a sequel to How to become an ML Engineer in 5 to 7 steps, where I covered my self-taught path toward becoming a machine learning engineer from a non-CS (though STEM) background. Here, I outline how I worked towards what I hope will be a career-defining role. I started this work after working in my first ML position for about a year. This is an account of my personal experiences, which I based on advice I got from friends and found online. I don’t claim it’s original, and my sample is n=1, so cherry-pick what resonates for you. I still hope some find it useful. To improve my chances of getting a career inflecting role, I think there are two different kinds of useful actions you can take: strategic and tactical ones. Tactical actions are relatively low effort, but with a high return in your specific situation. This may be reading up on the latest news on the company you are interviewing with, doing a couple of LeetCode problems to refresh muscle memory, doing mock interviews, or polishing your CV. Strategic actions are high effort, high return actions that may even seem fruitless in the specific moment, but in aggregate and compounding, give you a substantial advantage. Think about learning a new technology deeply by building a substantial portfolio project, having significant tenure at a reputable organization, building and maintaining a network, or building a personal brand by talking about your work. In the long run, it’s strategy that makes a successful career. But in each moment, there is often significant value in tactical work. Being prepared makes a good impression, and failing to get career-defining opportunities just because LeetCode is annoying is short-sighted.