The Only Coding Interview Guide You Need for Your MLE Interview Part 1

Wait — Who the Bleep Cares About My DSA?

If you’re an ML Engineer, chances are you’ve rolled your eyes at coding interviews. “I train deep learning models,” you think. “Why the heck do I need to know how to reverse a linked list?”

Because companies still care. And they’re not entirely wrong.

Even if you never touch a trie at work, your ability to solve algorithmic challenges is a proxy for core engineering skills. Do you break problems down efficiently? Can you recognize underlying patterns? Do you write clean, maintainable code?

This series is about preparing sanely for coding interviews. You are reading Part 1: Understanding DSA — what’s actually being tested, how to approach problems, and how to build a rock-solid foundation.

What’s Actually Being Tested?

A coding interview isn’t just about whether you can brute-force your way to a solution. It’s measuring how you think. Let’s break it down:

1. Can You Solve Hard Problems?

Your interviewer isn’t just looking for a solution. They want to see how you navigate ambiguity, adapt when things get tricky, and find the most efficient approach.

A brute-force answer might work, but if it’s O(N³) and the optimal solution is O(N log N), you’re in trouble.

2. Do You Know Your Fundamentals?

You’re expected to understand Big O complexity, recursion, common data structures, and when to use what. It’s not about memorizing every edge case for every algorithm — it’s about demonstrating a strong grasp of the building blocks of software engineering.

3. Can You Communicate Clearly?

Interviewers are listening for structured thinking. If your answer is right but you can’t explain how you got there, you’ll bomb the interview.

Think about it from their perspective: if they were stuck working with you, would they enjoy it? Would you make their life easier or harder?

Talk It Out — Your Interviewer Can’t Read Your Mind

Imagine you’re the interviewer. The candidate is scribbling furiously, completely silent. Minutes tick by. Are they onto something? Stuck? Just writing down a solution they memorized yesterday?

If you’re quiet for more than a minute or two, that’s a red flag.

Your interviewer isn’t psychic — they need to hear your thought process. Treat this like a real-world discussion with a coworker:

• Start by clarifying the problem.
• Walk through edge cases and constraints.
• Explain why you’re choosing a specific approach.
• If you hit a roadblock, talk through alternatives rather than staring at your screen in silence.

This isn’t just about making a good impression — it’s a sanity check for you. Talking through your logic can help you spot flaws before you even start coding. Or, if you can’t, often the interviewer will help you.

Pro tip: as an interviewer I would never say “Sure there isn’t a faster solution here?” if the answer is “There is no faster solution available.”

Do Not Fake Struggling or Mention You’ve Seen the Problem Before

Interviewers can smell BS a mile away.

• Don’t say, “Oh, I saw this exact problem before.” It kills the conversation and makes it look like you’re regurgitating a solution.
• Don’t fake struggle. If you know the answer, deliver it confidently. Just make sure to explain why it works instead of vomiting out a pre-memorized response.

Clarify the Problem Before You Touch the Keyboard

The fastest way to tank an interview? Misunderstanding the question.

I’ve seen candidates completely blow an otherwise solid performance by rushing into a solution without realizing they missed a crucial constraint.

Always confirm:
✅ What are the inputs and outputs?
✅ Are there any edge cases to watch out for?
✅ What are the constraints?

If you mess this up, nothing else matters — your solution won’t be answering the right question.

Plan First, Code Second

A strong candidate doesn’t just start coding — they map out their approach first.

Why?

• You don’t always see what you don’t see. Talking through your idea helps spot flaws early.
• If there’s a better approach, your interviewer might hint at it — but only if you communicate.
• Rushing in makes you more likely to write a messy, inefficient solution that’s harder to debug.
  A quick outline saves time. Do it.

Don’t Waste Time on Brute Force (Unless You Have To)

A brute-force solution is sometimes a stepping stone, but don’t lead with it unless you’re stuck.

• If a problem screams “dynamic programming” or “binary search,” start there.
• If you genuinely don’t see a better approach, state the brute-force method, but immediately look for optimizations.

The interviewer won’t be impressed if you dump an O(N³) solution when an O(N) one is possible.

Test Like an Engineer, Not a Robot

Testing isn’t just a formality — it’s how you prove your code works and how you catch your own mistakes before the interviewer does.

Bad candidates run trivial tests. Great candidates pick meaningful cases.

If time allows (and it should), try a manual dry run before you hit the “Run” button. Catching your own mistakes before the interviewer points them out is a massive plus.

BigO Notation

If you can’t analyze the efficiency of your solution, you aren’t ready for the interview.

Quick Refresher

• O(1) — Constant time (best case). Example: Hash table lookup.
• O(log N) — Logarithmic (fast). Example: Binary search.
• O(N) — Linear (scales with input size). Example: Single pass through an array.
• O(N log N) — Log-linear (common in sorting). Example: Merge sort, quicksort (average case).
• O(N²) and worse — Quadratic or worse (bad). Example: Nested loops over all elements.

Your goal is to optimize whenever possible. If your first pass is O(N²), find a way to get it down to O(N log N) or O(N).

I once failed a coding interview because I focused too much on finding O(N log N) solution where O(N²) was the best possible. Don’t overcomplicate your life, some problems require O(N²).

Worst, Best, and Average Cases
Always analyze the worst case. Some algorithms have great average-case performance but degrade in the worst case (e.g., quicksort). Be prepared to discuss all three.

Remember that there is space and time complexity and you can usually trade one for another.

Use Python — No Debate

MLEs, this isn’t a contest. Use Python.

• Fast to write — Less boilerplate, fewer syntax headaches.
• Rich standard library — Built-in data structures like sets, heaps, and deque save time.
• Readable — Your interviewer will thank you.

You can use another language, but unless you’re interviewing for a hardcore C++ role, Python gives you the best chance of writing clean, efficient solutions quickly.

How to Actually Prepare (Without Wasting Time)

Train Like an Athlete, Not a Random Grinder

Most people “prepare” by grinding hundreds of random LeetCode problems. That’s dumb. Here’s a better approach:

1. Start with structured problem sets: ,Blind 75, LeetCode Top 150, Company-tagged questions (LeetCode Premium)
2. Solve two problems per day.
3. Use spaced repetition. If you needed a hint, mark that problem and revisit in a few days.

Struggling? Use an LLM like ChatGPT for hints, not solutions. Example prompt: “Give me a hint for solving this problem, but don’t tell me the answer yet.”

Get Real With Mock Interviews

Solving problems alone is fine. But if you want to actually perform well in an interview, you need to simulate the real thing.

Mock interviews force you to:

• Explain your thought process out loud under pressure.
• Identify weak spots where your reasoning is unclear or where you struggle to transition from idea to implementation.
• Develop real-time problem-solving skills — because interviews don’t come with a pause button.

Why Mock Interviews Work (And Why LeetCode Grinding Isn’t Enough)

🚨 Warning: Some people grind 500+ problems but still fail interviews because they never practice talking through their solutions.

You might know the algorithm cold, but when an interviewer asks, “Why did you pick this approach?” or “Can you optimize it?” you need to articulate your reasoning clearly and concisely.

Mock interviews train you to think out loud, communicate efficiently, and get comfortable in high-stakes conversations — something that LeetCode alone can’t do. Aim to do at least 5.

How to Run a Mock Interview

If You’re the Candidate

1. Time Yourself Like a Real Interview 
   - 2 min: Clarify the problem, constraints, and edge cases. 
   - 3 min: Plan the approach and discuss trade-offs. 
   - 12 min: Write and debug code. 
   - 5 min: Analyze complexity, optimize, and test.
2. Narrate Your Thought Process 
   - Even when you pause to think, say something like, “I’m considering two approaches: a hash map for O(N) or sorting for O(N log N). Let’s compare…” 
   - If you hit a roadblock, talk through what’s blocking you rather than going silent.
3. Get Feedback from Your Partner 
   - What was clear? What was confusing? 
   - Did you take too long to get to the coding part? 
   - Did you miss an obvious optimization?

If You’re the Interviewer

Being the interviewer is just as valuable as being the candidate. It forces you to:

• Recognize good vs. bad explanations.
• Identify common mistakes (which will make you less likely to make them yourself).
• Understand multiple solutions to the same problem.

Step-by-Step Guide for the Interviewer

1. Pick a Problem and Solve It First 
   - Choose an Easy or Medium problem from LeetCode. 
   - Solve it yourself before the mock so you understand the solution space.
   - Look into and understand alternative solutions. Do not pressure your interviewee into the only solution you know, understand the space of solutions.
2. Set Expectations for the Session 
   - Tell your partner, “Treat this like a real interview. Talk through your thought process. I won’t give hints unless you ask.”
3. Listen for Structure, Not Just Correctness 
   - Is the candidate breaking the problem down logically? 
   - Are they considering edge cases early? 
   - Do they jump into coding too soon without planning?
4. Don’t Force Your Solution 
   - There are often multiple valid ways to solve a problem. 
   - Instead of saying, “That’s wrong,” ask, “How does this scale?” or “Can you optimize this further?”
5. Give Clear, Constructive Feedback 
   - Good: “Your approach works, but can you explain why it’s O(N log N) instead of O(N)?” 
   - Bad: “That’s wrong, do it my way.”
6. Manage time
   - If your interviewee is going too long on problem understanding, help them move on. You want to see them go through all stages, it helps no one for them to just run out of time.
7. Wrap Up with a Review 
   - Highlight one strength (e.g., “You clarified the problem well”). 
   - Highlight one area for improvement (e.g., “You should test with edge cases before assuming your code works”).

Next Up

This was about understanding DSA. Next time, we’ll talk about ML Coding interviews.