DSA by domain
AI & Machine Learning
The roles building models, vision and language systems — where the algorithmic core is math-, matrix- and DP-heavy.
What the roles here have in common
2 topics are core for every one of the 4 roles in AI & Machine Learning. Whatever job you end up interviewing for inside this domain, this is the part that was never optional:
- Arrays
- Math
11 more topics are core for some of these roles but not all of them. Which ones apply is exactly what changes when you pick your role below:
- Binary Search
- Dynamic Programming
- Graph
- Greedy
- Heap
- Matrix
- Queue
- Recursion
- Sliding Window
- Strings
- Trie
Roles in this domain
Same 370 problems, re-sequenced per role. Pick the exact one you are interviewing for — the roles with a full guide are linked; the rest work inside the app today.
- MLML loops tilt toward math and DP-flavoured optimisation rather than graph traversal: 6 core topics, 125 of our 370 problems, with matrices and math sitting in the core tier. Expect the DSA round to be mixed with applied stats and modelling questions elsewhere in the loop.
- Computer Vision EngineerIn the app
- NLP EngineerIn the app
- Robotics EngineerIn the app