For undergraduates outside the usual elite pipeline
Prepare for LLM technical interviews by learning the main line first.
This site keeps each topic short, practical, and testable. Read the 20 percent that appears most often, then answer from memory.
Burn the main line into muscle memory before touching optional reading.
Interview type map
Know what game you are playing.
Real interviews usually mix these six types. ML coding is the highest-frequency area for ML engineering interviews.
Roadmap
Foundation, core, systems, alignment, capstone.
Click a node to open its module. Mark modules complete as you finish the self-test.
Module library
One main line per module.
Each module has a short main line, one recall self-test, and optional reading hidden by default.
Practice
Train like the interview will feel.
Turn off AI assistance when you practice coding. Otherwise you will overestimate your skill.
Core muscle-memory project
Implement and debug a small Transformer from scratch. Treat Stanford CS336 Assignment 1 as the main training target.
- Write tensor shapes before coding.
- Build attention, MLP, layer norm, optimizer, and training loop.
- Debug with tiny batches and known shape checks.
- Repeat until you can rebuild the skeleton without help.
Algorithm base
Use LeetCode 75 or Neetcode Blind 75. The goal is fluency with arrays, hash maps, stacks, trees, graphs, and dynamic programming basics.
- Solve first without hints for 25 minutes.
- Explain time and memory complexity aloud.
- Rewrite missed problems after two days.
Technical discussion drill
Pick one topic, set a 12 minute timer, then answer as if a senior engineer is probing tradeoffs.
- Start with intuition.
- Name the failure mode.
- Give one experiment or metric.
Interview day and mindset
Preparation is technical and emotional.
Before the interview
Sleep matters more than late-night cramming. Prepare a short project story, a debugging story, and a failure story.
After the interview
Write notes immediately: questions, weak points, and what you would answer next time. This turns every interview into training data.
During the search
Job search can feel like a full-time job. Compare your process, not your luck, with other students.
Networking
The first interview often comes through a person.
This is not fair, but it is common. Build proof of work and make it easy for someone to refer you.
Join public ML communities and answer small questions consistently.
Contribute small fixes or docs to open-source ML projects.
Post short project notes on LinkedIn or X with clear results.
Message alumni with a specific ask and a link to proof of work.
Resources
Free resources, grouped by use.
This page is the only central resource pool. Module-specific links still live inside Go Deeper.