[New Grad] LG Electronics - Coding Test & Interview (Final Rejection) Recap

I originally posted this recap on Naver blog — moving it over to Tistory along with the migration.

Here's the short journey of my attempt to move to LG Electronics' Master's/PhD H&A Division (Gasan R&D Campus) as a researcher in H2 2022.

Application window: 2022.10.05 18:00 ~ 2022.10.24 23:00

Roles I Applied For

• 1st choice: AI — Control Intelligence, data analysis, cycle-analysis fusion
• 2nd choice: Software R&D — system-control-based algorithms, AI-based algorithms, motor control, autonomous-driving algorithms
• 3rd choice: Content server development — Backend service server development

These mapped to: 1st → Kitchen Appliances, 2nd → Living Appliances, 3rd → Platform Business.

Figure 1. LG Electronics document screening — passed

After passing the document screening, I was classified into SW track (since I applied to AI, SW R&D, server-dev roles), so I had to take both the aptitude test and the coding test. (LG Electronics requires both even for SW track.)

Aptitude + Coding Test Recap

Date: 2022-11-05

• Coding test: 10:00 - 12:00
• Aptitude test: 13:20 - 15:00

Figure 2. SW items

The aptitude test, like other major Korean conglomerates, consists of math, reasoning, language sections. Buying the LG Electronics aptitude prep book and going through it once was enough. The LG aptitude test isn't very hard. As for the coding test, I was already comfortable from internal competence tests at my current job, Programmers practice, and Samsung coding-test practice. I just spent one day reviewing problems I'd previously solved. I took the test in Java, my primary work language.

Coding Test Recap

3 problems in 2 hours — Programmers platform (2022-11-05, 10:00 - 12:00)

When you submit on Programmers, beyond the default sample test cases, it gave per-problem pass/fail feedback for all server-evaluated cases. So I knew which problems I'd nailed in real-time.

Problem 1 — Math pattern

A problem about quotient/remainder. Once you spot the rule, it solves quickly. (~7 minutes)

Problem 2 — Simulation / implementation

The kind of implementation problem Programmers seems to love lately. Running a game server where each server holds up to 5 characters; on exceeding the limit or duplicate name, rules govern updates. Straightforward implementation, but had to handle dedup and rule-based updates — essentially a CRUD over the server state. It took some time, but I got it on the first try with no debugging. (~1 hour)

Problem 3 — Search

Finally, the DFS / BFS problem that shows up in every coding test. This one was brute-force search with small input dimensions, so O(10²) brute force was feasible. Some edge cases needed attention.

I uploaded my reconstructed code to the following git repository for reference.

📝 The 3 .java files starting with 2022-11-05- are the reconstructed problems.

CodingTestReproduction · taehyuklee/AlgorithmRepo tracking problems solved on online judges (Baekjoon · Programmers · Softeer · CodeTree).github.com

10:00-12:00 was the coding test; 13:20-15:00 was the aptitude test.

Figure 3. Coding test + aptitude result

I passed both the coding test and aptitude test and got the 1st interview invitation. The result came out on November 11, about a week later — the process moved faster than I expected.

1st Interview Recap (Job Interview)

The 1st interview was research-centered, given the Master's/PhD nature of the role — focused on what I did during my Master's/PhD. You build a max-6-page PPT (cover included) in LG's template based on your application's research description, present it, and defend it.

👥 Interview panel — 1 team-lead researcher + 1 senior researcher + 1 HR (3 total)

Page structure: - Page 1 — Cover - Page 2 — Application motivation + experience overview - Pages 3-6 — My research explanation

My research was applying reinforcement learning to fluid simulations to discover a new control method and generalize the model. As you can see, I was targeting the Control Intelligence role. I introduced myself using my current IT knowledge/experience, then focused the rest on my research.

I wanted to combine all my abilities and on-the-job experience, going in with 4 keywords: Mechanical, AI, Data Analysis, IT. Technical questions on my research, mechanical engineering knowledge, and AI were all answered without much trouble. Because I claimed experience across a wide range of fields, there was significant verification on each — and I felt I defended them all reasonably well.

Finally, there was the question: "You majored in mechanical engineering, so why IT?" I answered as follows.

I wrapped up the 1st interview that way. The 30 minutes flew by, and the interview felt mainly oriented toward verifying my background claims.

Figure 4. 1st interview result

I passed the 1st interview, and unusually, they asked me to do a medical check-up (hiring check-up) before the 2nd interview. Going to a hiring check-up usually means the company already feels confident — barring a slip-up in the 2nd interview, it's likely a pass.

📝 Note: the 2nd interview was in-person at the actual site, and they reimbursed travel costs.

2nd Interview Recap (Executive + English Interview)

The 2nd interview consisted of an executive interview and an English interview.

Executive Interview Recap

I went in with prepared talking points researched for the executive interview.

One of my talking points

I came in with various talking points and lines of reasoning like this.

I also thought through likely follow-up questions: How would I use data from home appliances? How to build the system? What benefits when AI is integrated? I prepared concrete scenarios. I even thought about which problems could be solved by combining with mechanical-engineering knowledge. I actually delivered all this in the interview as planned, and the atmosphere felt receptive.

But one question from the executive sitting in the middle threw me off:

The question made me feel a creeping disconnect from the AI / SW R&D / server-dev roles I'd applied for.

I majored in mechanical engineering for both undergrad and grad school. But I was doing IT and AI alongside, to handle fusion work beyond just mechanical. My intent in applying for the SW track was to collect / aggregate data from electronics and machinery, generate insights, predict the future, and inform decisions. That's the goal I'd been preparing toward, that's the role I applied to, and I'd gone through the matching process for that.

My expression slowly hardened. Knowing how critical the actual role you'll be doing matters at a company, I had to make a decision. I asked, at the end:

HR answered for them:

From the company's perspective, that's not wrong. The moment I asked and heard that answer, I got the rough sense: "Ah... I'm likely not getting through the final interview." And as expected, I was rejected.

Figure 5. 2nd interview result

Well, it was my choice and a regret-free interview. Maybe that's why the rejection didn't sting much — I returned to reality thinking I'd grow at my current job and that better opportunities would come.

English Interview

The English interview was 1:1 with a native English speaker over a Zoom-like tool.

📝 If you prepare around OPIc level, you'll be fine.

Wrap-up

The LG Electronics journey gave me a chance to revisit my own thinking and life goals one more time. :)