Lessons learned from a Machine Learning Engineer:
When looking for an ML position, is better to research about the industry in which the company operates than the ML title itself. A Computer Vision job can be hidden by a Software Engineer - C++ title.
Training models is just a tiny task of your job. During this year, I had to focus more on infrastructure, performance and scalability than building and actual model. Plus I highly recommend to have a strong SW background for this, and it was surprisingly cool to learn.
Keep it simple, there are a lot of Machine Learning tools that are accesible and easy to use. The challenge will come when integrating the tools/models into your company’s production pipeline.
“It’s all about the data” did not apply to me this year. I see companies focusing more on Augmented Intelligence rather than Artificial Intelligence. As ML you might end up building tools for people to easily use ML rather than building ML itself.
If you want to work with something specific, you might want to do that as a side project. Don’t wait until you get your desired ML position, you can start working on it from today!