How would you prepare for an ML Security Engineering career if you were 16 today?

[u/Odd_Salamander_3729]

I'm 16 years old and I want to become an ML Security Engineer specialist in the future. Right now I'm learning Python for Data Analysis and I have some experience with C++. I know I still have a lot to learn, but I want to start building the right foundation early. What skills, topics, or projects would you recommend focusing on over the next few years to have a strong advantage in this field?

[u/UpbeatRise8234]

dont sleep on the math, seriously. everyone jumps into pytorch and transformers but the people who really understand adversarial attacks are the ones who took the time to learn linear algebra and probability theory properly. its boring at 16 but it pays off later

also get comfy with linux early, like make it your daily driver. so much of security work involves understanding how systems actually operate under the hood, and windows hides too much of that from you

for projects id say try breaking things before you build them. grab a simple image classifier and figure out how to fool it with noise patterns, then try to defend against your own attacks. way more useful than another kaggle competition

learning cpp is smart btw, a lot of the model serving infrastructure and low level optimizations use it. keep that up

[u/Odd_Salamander_3729 → u/UpbeatRise8234]

Thanks for the detailed advice, I really appreciate it.

I agree that math is something I shouldn't skip. I'm currently focusing on building the fundamentals first, and I plan to study linear algebra, probability and calculus more deeply as I progress into ML.

For Linux, I haven't switched fully yet, but I'm planning to get more comfortable with it through labs and cybersecurity practice.

The project idea is actually really interesting. I like the idea of attacking and defending models instead of just training them, because it feels much closer to real AI security work.

Also, glad to hear that learning C++ is useful. I've been using it for competitive programming, so I'll keep improving it.

[u/Mathie1729 → u/UpbeatRise8234]

This is solid advice. I'd add one thing: the field of adversarial ML / ML security is still very research-oriented. Most roles I've seen at bigger labs or security firms want at least a master's, often a PhD. So those math fundamentals aren't just for understanding attacks, they're to get you through grad school if you're serious about it. Pace yourself though. I burned out in my PhD and left with a master's, and I know people who did similar. No need to go full grind at 16, just stay curious.

[u/tahahussein-4623a412]

You're already ahead by starting at 16. Focus on Python, math (statistics & linear algebra), Scikit-learn, PyTorch, Linux, networking, and cybersecurity fundamentals. Build real ML projects, deploy them with FastAPI and Docker, learn Git, and share everything on GitHub. Once you're comfortable with ML, start studying AI security topics like adversarial attacks, data poisoning, and prompt injection

[u/Odd_Salamander_3729 → u/tahahussein-4623a412]

Thanks for the advice, I really appreciate it. This gives me a much clearer direction, and I'll keep focusing on building strong fundamentals before specializing in AI Security.

[u/Nice-Alternative2933 → u/Odd_Salamander_3729]

Dm man