How to land an ML job: Advice from engineers at Meta, Google Brain, and SAP - KDnuggets

Kaushik is a technical leader at Meta, and has over 10 years of experience building AI-driven products at companies like LinkedIn and Google. Shalvi is an AI scientist at SAP, and has experience as a data scientist, a software engineer, and project manager. Frank is a founding engineer at co:rise and started his career at Coursera, where he was the first engineering hire and built much of the platform's original core infrastructure. The following excerpts from Jake's conversation with Kaushik, Shalvi, and Frank have been edited and condensed for clarity. You can watch the complete recording here. Kaushik, you've been a hiring manager at some big companies. You get a lot of resumes. What are you looking for? What advice do you have for someone who's working on their resume and thinking about how to position themselves? Kaushik: In terms of skills, I'm looking for a practical knowledge of applying ML to build products. That's something I think you can't get from books -- you have to have some hands-on experience. I'm not necessarily looking for someone to have experience with specific tools or techniques, because those things are constantly changing. It's more that I want to know about the approach they took. Why did they use the tools they did, and what did they do when things got tricky or didn't work the first time? Don't get me wrong, I think having a good theoretical foundation is definitely necessary. But I would say you should spend as much time as you can solving real problems. That's how you learn which techniques work best for which use cases, and it will help you get a better understanding of the theoretical side, too. Kaushik: In terms of preparing for interviews, other than brushing up on the fundamentals, my advice would be to brainstorm a couple of problems that are relevant to the company you're interviewing with and do some background research on the common techniques to solve those problems.