I’m a Computer Science graduate preparing for ML/AI Engineer roles. I’m facing a dilemma about what to focus on, how much to allocate time to each area, and what exact roadmap to follow to prepare effectively.
Based on what I’ve learned, ML Engineer interviews usually cover:
Coding / DSA: LeetCode-style problems, data structures, and algorithms.
ML Fundamentals: Concepts like Logistic Regression, SVMs, Random Forests, etc.
Implementation: PyTorch model building, training loops, and data pipeline design.
Applied Work: Kaggle or personal projects that demonstrate real-world understanding.
My challenge is — I’m not strong at DSA, but I’m comfortable with Python and ML basics. I want to become industry-ready for ML Engineer roles in the next few months, but I often get stuck deciding how much time to devote to:
DSA / Coding problems
ML theory & mathematics
PyTorch implementation and system design
Kaggle competitions or projects
I’d like to hear from professionals who’ve already gone through this journey:
How did you personally structure your preparation time among these areas?
If you were in my position today (moderate ML knowledge, weak DSA), what would your weekly schedule or roadmap look like?
Should I build depth first in DSA or maintain a balance with projects?
Any experience-based insights, timelines, or practical breakdowns?
(I’m not looking for generic advice like “do both”; I’d love to hear concrete “this is what worked for me” type of responses.)