The purpose of this post is to get you ramp up with your next Machine Learning System Design Interview.
Bear with me, and I will show you how to get yourself prepared with this interview.

Great Resources:

1. Machine Learning Life Cycle
2. System Design Interview (for general software, but really good!)

Steps:

The following are the steps I used to get myself ready for the ML System Design interview:

0. Master the Fundamentals

If you have time (3+ months), I strongly recommend you brush up on the fundamentals. Having a strong foundation is essential and will help you acquire deeper more complex knowledge as you dives deeper in this field. Fundamentals I recommend you to possess:

1. Categories of Machine Learning (Supervised/Unsupervised/Reinforcement Learning)
2. The Bias / Variance Tradeoff
3. Linear Regression and linear classifier (Multi-layer Perceptron)
4. Loss function & Optimizers (Gradient Descent and its variants)
5. Neural Networks, Convolutional Neural Networks (CNN)
   • Why deeper is better than wider?
6. Recurrent Neural Networks, GRU, LSTM
7. Attention mechanism and Transformer (*)
   • What’re the difference between Transformer v.s. CNN architecture? Pros & Cons.
8. Vision Transformer (ViT), Detection Transformer (DETR) , Deformable DETR
9. CLIP: Contrastive Learning to learn alignment between Language and Image modalities.
10. Foundation Models:
    • LLM
    • VLM
    • VLA for robotics/autonomous driving.

If you are interviewing for some roles that require specific domain of ML knowlege, for example:

• Generative models:
  • GAN
  • Diffusion Models
  • LLM & VLM
• Reinforcement Learning:
  • Dinamic programming
  • SARSA v.s. Q-Learning v.s. TD Learning
  • Deep Q Network (discrete action space)
  • Deep Deterministic Policy Gradient (DDPG: Continuous action space)
  • RLHF: RL with Human Feedback

1. Master the Framework.

Be super familiar with the ML System Design Framework. I highly recommend you follow the ML Life Cycle to set up your own framework. Having a strong framework can greatly reduce your stress and help you navigate through the interview when you start to feel lost.

This is the framework I use, and I like it because it’s a flywheel:

2. Practice with Case Studies

AI is your best friend. Use any tool you like – ChatGPT, Gemini – ask them to give you some examples of the interview questions/prompts. Work on the problems on your own. Iterate on your answers and familiar yourself with the answer.

For example, if you’re interviewing a perception role, some sample question coule be: “Design a system that can detect pedestrians and other traffic participants in real time for a self-driving car.”, “Design a traffic signal and traffic sign detection system for an autonomous vehicle.”

3. Mock Interviews

MOCK! MOCK! MOCK! This is CRUCIAL so I have to emphasize the word 3 times.

This is the most important part in the interview prep. Always do mock interviews before the actual interview! Find your friends/trusted connections to help you with mock interviews. Set the mock interview to be as realistic as possible: time the interview(計時), set your desk like it’s real interview. You can even record it and review it once you finish it. Trust me – You can easily find parts where you can improve on through this step.

Next, we’ll talk about how to nail the Machine Learning Coding Interview.

背景

我們成長的過程，努力的讀書，學習，做筆記。無論是傳統的手寫筆記，嘗試理解一段數學式或是一套程式碼。到現在盛行數位筆記，能夠動態新增/修改筆記內容，也能透過網路媒體分享到世界各地。

但是，當我捫心自問：這當中的筆記，有多少我真正回頭去檢視複習過？

在以前學生時代，應付考試，每次的考試有『範圍』限制，因此多少知道該從哪裡讀到哪裡。
當時寫筆記，在大考當日前也許有時間拿出來翻個兩三次複習。

如今，進入職場後，每一次的面試，頂多知道考試的『主題』與『方向』。當領域一擴大，以前熟悉的『範圍』概念徹底解崩，突然間不知道該從哪裡開始著手。

我個人過去準備面試的經驗，是從自己知道的概念開始準備，一邊複習以前熟悉的概念，一邊學習新知，同時加強不熟習的區塊。但我遇到很大的問題：筆記的數量無限增加，發散，無法收斂成精。

導致在面試前其實很難回來重看一路上整理的所有筆記。

我的痛點：想要有一套可以一直持續複習的基礎知識系統，協助我精進專業領域知識。
I want a system to keep me sharp and solid on the fundamentals of the domain knowledge I want to master.
For example, machine learning (for computer vision and autonomous driving).

我的 2025 備戰與面試心得文章可參考： 自動駕駛求職記錄（在職跳槽）