How hard is it to break into ML work without a Master's degree? [D]

I'm currently a software engineer (mostly mobile/iOS development) and have recently started learning machine learning because I genuinely find it interesting, especially the math behind it.

I have a fairly strong math background and am comfortable with calculus, probability, and math in general. Right now I'm learning through a combination of Andrew Ng's ML course and Stanford CS229. My plan is to build some projects once I have a stronger foundation.

What attracts me to ML is the mathematics behind it. My goal isn't just to use existing libraries to train models and tune hyperparameters; I want to understand the underlying theory, algorithms, and reasoning that make these models work. I'm interested in going deeper into the field rather than treating ML as a black box.

That said, I keep seeing ML roles that prefer or require a Master's or PhD, so I'm trying to understand how realistic this path is.

For people who have successfully made the switch:

• Did you have a Master's/PhD, or were you self-taught?
• How difficult was it to get interviews without an advanced degree?
• What types of projects helped you stand out?
• Did you transition into ML engineering first, or directly into more model-focused work?
• What level of math and statistics do you actually use on the job?
• If you were starting again today as a software engineer with a strong math background, what path would you follow?

I'm looking for honest experiences, including failures and challenges, not just success stories.