I mean offer as in offer and demand. There isn't many people with the engineering skills, ML expertise and on-the-job experience. The barrier for entry means t

I mean offer as in offer and demand. There isn't many people with the engineering skills, ML expertise and on-the-job experience. The barrier for entry means that interviewing for the job, even with experience, is very difficult. As others said maybe the different is there is huge demand. Still I find it paradoxical. You'd think having experience in a field with high demand and low offer would be a pretty good deal. And yet it would be easier for me to find a job as a generalist than as an specialist. My advice to get in is based on my experience so YMMV. I'd say the easiest way is to transfer internally to an ML position. That's how I got here. If you are a solid engineer, teams are happy to fill in your ML knowledge gaps. Anecdotically most people I interview got to ML the same way. If your company doesn't have official ML Engineer roles or ML projects, you can position yourself to jump in when the opportunity arises, or even pitch a project yourself (leadership seems to be suckers for ML-for-anything). The second easiest way is as a student/new grad/junior. Expectations for juniors are lower so it's always easier to get in. The ML round of the interview is mostly theoretical so students aren't at a disadvantage.