Do you want to:
a) Become an academic in mathematics/statistics.
b) Become an academic in computer science with a focus on artificial intelligence.
c) Become a MLE in "regular" statistical applications. Aka bayesian classification, "core" statistical principles.
d) Become a specialized computer vision/natural language processing focused MLE.
e) Become a generalist software engineer who can whip out the above if needed.
In no way is e) the inferior option.
Generalists who can write code fast with 100% test coverage and pristine logging are by far the segment the industry has the shortest supply of.
There are TONS of math guys. Vanishingly few Principal Engineers who can write a design document and lead a project.
(Machine learning customers are OBSESSED with test coverage and verifiability. Believe it or not, multinational corporations generally don't want to unleash a {your_adjective_here}ist algorithm on the world.)
2. Study the above, properly.
To study the math, Elements of Statistical Learning/Algorithms by Goodfellow.
Start on page 1, do every second exercise. Publish a summary of every chapter you finish with your answers to GitHub.
3. Pursue your goal in a publicly verifiable manner.
See: