Simplifying complexity, executing to completion

Discover how professionals are growing with Lablup

Soyeong Lim

Jinho Heo

Eunjin Hwang, Hyunhoi Koo, Sergey Leksikov

Charting what's next, today

At Lablup, there is a team whose work does not land in the current product but shapes what comes next. The Research team looks six months, or even one or two years, ahead of the tools and capabilities Backend.AI may need. Across three labs (GenAI Lab, Model & Data Lab, and NACL), each researcher takes on a different topic, and together they bring those topics into a shared direction. A Principal Researcher with a PhD in physics, a junior researcher who returned to Korea after working at a trading firm in the Netherlands, and a researcher from overseas who finished his master's at KAIST before joining Lablup. We sat down with the three of them to talk about what they are building and how the team comes together.

## Q. Could you introduce yourselves?
![인터뷰콘텐츠 자기소개_ENG.png](https://cdn.lablup.com/ENG_f6ce056d84.png)
**Eunjin |** Hello, I am Eunjin Hwang, and I lead the Research team at Lablup as a Principal Researcher. Lablup is my first company. I spent most of my early career in academia, finished my PhD, and then did a postdoctoral fellowship at KIST before moving to Lablup. My PhD is in physics, specifically in a branch of statistical physics called complex systems, which studies how many different components interact to create complex phenomena. Typical examples in complex systems of physics include economic systems, social phenomena, and brain and cognitive functions. During my PhD, I worked on classifying and predicting brain states from EEG (electroencephalogram) signals. Spending so much time on natural intelligence and cognition made me curious about artificial intelligence as well, and that is how I ended up joining the Research team at Lablup.

**Hyunhoi |** Hello, I am Hyunhoi Koo, and I work in NACL (Next Generation Accelerated Computing Lab) on the Research team. Before joining Lablup, I did an integrated bachelor's and master's program at Imperial College London. During that time, I interned at Google for three months and at Citadel Securities for six months. After graduating, I moved to the Netherlands and worked at IMC Trading, a proprietary trading firm, for about two years. I recently came back to Korea and joined Lablup through the Technical Research Personnel program. AI infrastructure is new territory for me, but I am really enjoying the work.

**Sergey |** I am Sergey Leksikov. Before joining Lablup in 2020, I did my master's in Knowledge Service Engineering at KAIST, and after graduating I spent about three years as a data scientist at a fintech startup working with ESG (Environmental, Social, and Governance) data. Right now, I am in the GenAI lab on the Research team, building applications on top of generative AI.

## Q. What made each of you decide to join Lablup?
![래블업에 합류하게 된 계기.jpg](https://cdn.lablup.com/_a6682c78e5.jpg)
**Eunjin |** It came together without a formal application. What Lablup was looking for and where my career stood after my postdoc happened to line up. Lablup was planning to set up its corporate research institute and needed PhD-level researchers, and I had been collaborating with Lablup on a project during my postdoc at KIST. When the KIST contract ended, the project still needed to continue. Moving over to Lablup while contributing to the new research institute was a natural fit. That was March 2017, so I was essentially the first hire outside the founders.

**Hyunhoi |** I was working in high-frequency trading at IMC Trading in the Netherlands. It is a field where trade speed determines everything, so the technical bar on cutting latency is extremely high. I became curious about the AI industry, and when I looked into it, I realized that for AI training and inference, the key metric is not latency but throughput, the number of operations per unit of time. Both fields deal with performance, but the specific thing I was measuring is completely different, and the contrast was actually interesting to me. Around the time I was moving back to Korea, an opportunity to join Lablup through the Technical Research Personnel program came up, so I took it.

**Sergey |** In 2020, I looked through Lablup and Backend.AI's webpage, and I also checked the LinkedIn profiles of people working there including Joongi and Jeongkyu. I could see they were genuine experts in their fields, with experience that ran from teaching as professors to hands-on work across hardware and software. The senior members of the team were engineers and technologists rather than business or management people, which I really liked. The code repository that I checked through GitHub looked well organized, and I wanted to work with experts in this area. The company was already using English as its working language and had plans to expand globally. I really wanted to work here for these reasons.

## Q. How is the Research team organized, and what is each lab working on?
![팀 내외부.jpg](https://cdn.lablup.com/_6a4073e84f.jpg)
**Eunjin |** The Research team is made up of three labs. The GenAI lab builds different applications on top of generative AI. The Model & Data lab develops our own language models, including Korean language models. And NACL, the Next Generation Accelerated Computing Lab, looks ahead at new features that could go into Backend.AI and carries them through to implementation. We do not think the whole team needs to move in one direction, so the three labs take on very different topics. We encourage people in our team to bring ideas from the bottom up. There is top-down work too, some of it driven by customer requests, but the way each lab runs is closer to setting up guardrails, so each person can carry their own topic.

**Eunjin |** This structure comes from my own experience. My postdoc advisor was great at this. His lab worked on computational neuroscience, but the researchers were on completely different topics: sleep, epilepsy, and dementia. He was the one who wove them into a single story for the outside world. When I thought about what the Research team at Lablup should look like, the image came back to me. Honestly, I am not in a position to tell this team what to do. (laughs) The people on this team have far more expertise in their own areas than I do, and they could lead anywhere. My job is to set the stage so they can bring their ideas to life and to connect them with the resources they need.

## Q. What project are each of you most focused on right now?
![지금 집중하고 계신 프로젝트.jpg](https://cdn.lablup.com/_f0e9a2c6dd.jpg)
**Sergey |** Right now, I am focused on a RAG (Retrieval-Augmented Generation) agent. Our APC (AI Platform and Consulting) team and GTM (Go-To-Market) team meet a lot of clients, and they needed a better way to handle customer support efficiently. The Research team decided to take on that internal problem. We have a knowledge base built from past customer questions and answers, and each product's documentation is on GitHub. If the AI agent can read through all of that and give clients a trustworthy answer grounded in our own materials, that helps each team directly. Once we started building it, we saw that the data is large and varied, so I read the latest papers and check recent use cases on X (formerly Twitter) regularly, and I try approaches that might improve efficiency and accuracy in our own setting.

**Hyunhoi |** My main project right now is Lagrange, an early-stage research project in NACL that integrates Kubernetes with Backend.AI. The name comes from orbital mechanics. Backend.AI is one large celestial body, Kubernetes is another, and Lagrange is the module that sits between them like a space station. A Lagrange point is where the gravitational forces of two bodies balance, and we borrowed the name from there. Kubernetes was originally closer to an orchestrator for microservices and other web workloads. As AI workloads grew, with long-running training and inference jobs that needed a stable place to live, Kubernetes took on a wider role. Today it feels less like a pure orchestrator and more like an infrastructure OS. From our side, Kubernetes is no longer a competitor. It is a platform we can work alongside. Lagrange's approach is to sync the state of a Backend.AI cluster into Kubernetes and show a mirrored version on the Kubernetes side.

## Q. How does a project run day to day? How does collaboration inside the team, and communication with other teams, flow?
![팀 내외부.jpg](https://cdn.lablup.com/_6a4073e84f.jpg)
**Hyunhoi |** Daemyung Kang and I work closely on the Lagrange project. I am about three years into my career, while Daemyung has more than twenty years of experience as a developer, so he is both a teammate and someone close to a mentor. Inside the team, we hold a short daily stand-up of about fifteen minutes at a set time. Beyond that, we rely heavily on asynchronous communication. In async communication, there is a separation, a decoupling, between when a message is sent and when it is received, which means we have to write carefully and precisely. We use that kind of written context to keep our shared understanding sharp.

**Eunjin |** Inside the Research team, we hold a weekly sync-up meeting to share progress. This month, for the first time, we held what we are calling a Research Team Seminar Day, where the research team presented our work to the wider company. We plan to make it a regular format, both to share our research and to collect feedback from other teams. Beyond that, communication with other teams happens on Teams channels as needed. Since the Research team does not sit at the customer interface, we stay in touch with the Sales and GTM teams regularly to check whether their read of what customers want matches the direction our research is heading.

**Hyunhoi |** For NACL specifically, we meet with the CoreDev team every two weeks to line up the direction for whatever comes next after Lagrange. We talk through what we are considering, where it matches customer requests, where it does not, and where we should adjust. It has become a regular exchange.

## Q. Was there a recent moment where you felt you grew the most?

**Sergey |** The HS code classification project is what comes to mind. That kind of work usually gets split across a data engineer, an ML engineer, and a data analyst, but I ended up leading the whole thing myself, with some help from interns. Designing and building the entire pipeline from end to end was a clear step for me.

**Eunjin |** The first time I led a government research project, back in 2021. It was called AI Convergence for Emerging Infectious Disease Response, and within it I was responsible for a subproject analyzing parameters of disease transmission. At the proposal stage, I was the only person available to take on the project lead role. Fortunately, Sergey did excellent modeling work, and the other institutions in our consortium stepped in when we needed them, so we closed out the three-year project cleanly. That experience taught me that what feels impossible starts to work once you actually start, and that a role can shape the person filling it. I grew a lot from it.

**Eunjin |** The Dokpamo (Independent AI Foundation Model) project was another big growth moment for me. Watching the CoreDev team handle large-scale cluster operations from the side, the overall structure of our own product started to click. Before that, I only used Backend.AI as a user, so if the Jupyter Notebook launched, I did not have to worry about how the pieces fit together behind it. In the past, if something went wrong, I would say something like, "the session menu in our R&D cloud is not loading." Now I can say something more specific: "looks like the NFS mount (the network storage that holds models, data, and code) has disconnected, could someone take a look?" It sounds like a small thing, but it was a meaningful change for me.

## Q. Has there been a project that went in an unexpected direction, or that you would describe as a failure?

**Hyunhoi |** Lagrange itself is a good example. Our starting assumption was that customers would strongly want to see Backend.AI's detailed workload and node state inside Kubernetes, one-to-one. That is why we designed it to mirror the state of a Backend.AI cluster into Kubernetes. Recently, when we synced with other teams, we learned that this specific demand from customers is smaller than we had expected. If that is the case, workload-level one-to-one mirroring might not be the best path. Lately, we are also looking at a different direction where Backend.AI runs on top of Kubernetes as a platform but keeps its orchestration role independent. I am continuing to work on Lagrange, and Daemyung is exploring the feasibility of the new direction.

**Eunjin |** When an unexpected shift happens, our approach is to talk to the other teams who know the area well and search for the best path together. Even if it means rewriting the original direction, if that turns out to be the better choice, we prefer to adjust quickly.

## Q. What do you hope the Research team will look like one or two years from now?
![1-2년 뒤.jpg](https://cdn.lablup.com/1_2_b3f589d822.jpg)
**Eunjin |** Personally, I would love the Research team to become a team that can take on areas we have not explored yet. Domains like physical AI or agents that Backend.AI has not yet been shaped around. Improving what we already do well is something the CoreDev team handles excellently. What I would rather do on our side is look a step further out. If we can surface use cases that Backend.AI was not originally designed for, that would be exciting.

**Hyunhoi |** These days, it is hard to predict the future with any confidence. The industry is moving at an almost absurd pace, and what we think will matter in six months might be completely different from what matters when that time comes. Therefore, I think what the Research team can do right now, in the present, is just as important as preparing for what is coming.

## Q. What kind of person do you hope to join the Research team?
![여러분이 생각하는 리서치팀의 인재상.jpg](https://cdn.lablup.com/_4f9c6762b6.jpg)
**Eunjin |** Not necessarily a specialist in a specific domain. It is more important to be someone who can surface their own ideas from the bottom up and carry them forward themselves. This is genuinely how the Research team runs. Someone who can think freely and flexibly, bring a lot of ideas to the table, and carry their own work through would be ideal.

**Sergey |** I hope for someone with real curiosity. Someone who enjoys learning new things, takes interest across many fields, and keeps adding to what they know. That quality matters a lot to me in a future teammate.

**Hyunhoi |** Two things come to mind. First, you need to be proactive. On the Research team, the ideas have to come from you: "this should happen," or "this might work." We are a bottom-up, open team, so there is almost no micromanagement. Second, you should really enjoy what you do. If you find new things exciting, you will fit well here. Even as a junior, I have been given real responsibility and room to run with projects, and the work is genuinely fun for me.

* * *

This interview was conducted on April 16, 2026.

**Interviewee** &nbsp; Eunjin Hwang, Hyunhoi Koo, Sergey Leksikov
**Interviewer and Photographer** &nbsp; Jinho Heo
**Editor** &nbsp; Soyeong Lim

### Join Lablup!

[See open positions](https://www.lablup.com/careers)

---

#### Read more

- [Building Backend.AI from its core | Hyeokjin Kim, Bokeum Kim](https://www.lablup.com/blog/career/2025-12-coredev-interview)
- [Where questions are welcome more than answers - Inside FrontendDev | Jongeun Lee, Sujin Kim](https://www.lablup.com/blog/career/2025-07-frontend-team-interview)

Research team developing Lablup's future technology

Career | up

Charting what's next, today | Eunjin Hwang, Hyunhoi Koo, Sergey Leksikov

Lablup

Hyeokjin Kim, Bokeum Kim

Building Backend.AI from its core

There are people who quietly, yet firmly, support the system from unseen places. The CoreDev team, which takes charge of Backend.AI’s heart, is responsible for designing the platform’s stable foundation and laying the groundwork for new features. Hyeokjin and Bokeum, who continue deep technical discussions and coordinate the team’s speed and efficiency, were interviewed to explore the world they are building.

# Q. Please introduce yourselves.
![CoreDev Profile.jpg](https://cdn.lablup.com/Core_Dev_Profile_6cc253902e.jpg)
**Hyeokjin |** I am Hyeokjin Kim, leading the CoreDev team and developing various core functions of Backend.AI.

**Bokeum |** I am Bokeum Kim, working alongside Hyeokjin in the CoreDev team, focusing on Backend.AI’s new feature development and maintenance.

# Q. What led you to join Lablup?

**Hyeokjin |** I originally worked in media development, but it was hard to see tangible results. The only noticeable achievement was slightly reducing loading times on broadcasts, which did not make a big service-level difference. I wanted to find a field where my efforts could lead to visible results. At the time, AI was rapidly growing, and the technical aspects seemed very interesting, so I joined Lablup, which is close to the infrastructure of AI.

**Bokeum |** I first learned about Lablup two years ago at the AI Expo. When I heard about the Backend.AI service, I felt it was exactly the kind of software needed in today’s AI-driven era. A friend who had interned at Lablup recommended the company, which led me to join.

# Q. Why did you choose backend development among many development fields?

**Hyeokjin |** I believe backend is the core area for actual feature implementation. I have always enjoyed thinking about how to implement features more efficiently. Even in my first job in the media field, I chose backend because I wanted to work on deeper aspects.

**Bokeum |** I started my career by planning and operating AI engineer training programs. I wanted to create an environment where technology and AI could solve problems. While managing education, I wanted to become an engineer and contribute directly to products and ecosystems. I tried various fields, including data analysis and data engineering, but I found backend and infrastructure work most enjoyable, which led me here.

# Q. Can you explain in detail what the CoreDev team does?
![A7205952.jpg](https://cdn.lablup.com/A7205952_e99b0d21e2.jpg)
**Hyeokjin |** The CoreDev team develops the core of Backend.AI. Our biggest goal is to create a stable service that incorporates both existing and new features. When adding or improving a feature, it is not enough to work only on the frontend or API side. Sometimes, unexpected scenarios occur, requiring us to rewrite internal code structures or overhaul existing code for new features. The CoreDev team takes the lead in these internal improvements and new feature development. You could say we are the team that breaks the ground.

# Q. What languages and frameworks does the CoreDev team mainly use for development?
![A7205939 복사.jpg](https://cdn.lablup.com/A7205939_d376585fd3.jpg)
**Bokeum |** The CoreDev team mainly uses Python. While most backend teams use synchronous libraries or frameworks, we use aiohttp, an asynchronous web framework, for our code. We also use GraphQL server libraries to support both REST and GraphQL communication.

# Q. Please introduce one recent project you have been focusing on.

**Hyeokjin |** I am managing how to structure and complete epic tasks, which are broad and long-term requirements in software development, and maximizing the team’s efficiency to finish within deadlines. Recently, I improved the Redis usage pattern and separated layers to clearly identify error sources. Bugs can occur due to human error, but it is more problematic if we cannot detect them. I implemented Prometheus metrics collection at the API and service layers, added error codes, and applied OpenTelemetry to collect logs.

**Bokeum |** I have been working on making Backend.AI’s structure easier to maintain. For example, there are some tightly coupled features. These may work well now, but they can be hard to modify or test individually later. If there are many such codes, it becomes difficult to find the cause of bugs, and fixing one bug might cause side effects elsewhere. I mainly work on separating code by responsibility and role to prevent these issues beforehand.

# Q. Are there any coding principles that the CoreDev team values the most?

**Hyeokjin |** When I look at code, I value minimizing worries about whether it will be properly organized or whether unexpected situations will occur. It is important to create a common structure that guarantees consistent behavior for all code within it. For example, if a resource is opened, it must be guaranteed to be closed, and structures that minimize human error are essential.

**Bokeum |** I value writing clear PR titles and descriptions so that reviewers can immediately understand what feature is being added or what is being fixed, without needing to search through other code. I also emphasize separating concerns so that modifying one piece of code does not cause side effects elsewhere, and defining common behaviors to simplify tasks.

# Q. Does Lablup use AI in development or maintenance?
![A7206055.jpg](https://cdn.lablup.com/A7206055_4d67663335.jpg)
**Hyeokjin |** We use Claude Code in various parts of our development flow, including refactoring, maintenance, and documentation. We are trying various ways to make the process of using AI for code writing as stable as possible. When working with AI, it is crucial to clearly understand the direction of the code. If you can explain this well to AI, it can produce quite good results. Previously, writing code took some time, but now the time spent on writing code has been reduced to almost nothing. However, development speed does not increase as much as expected because review time and the time needed to understand code do not decrease much. We are internally trying various approaches to minimize these bottlenecks.

**Bokeum |** After defining the task, I clarify the implementation details and specifications through conversations with Claude, then proceed with the implementation in the form of Claude Code. In the Backend.AI repository, we manage files like CLAUDE.md to create an environment where agents can consistently write code. Finally, we require at least two reviewers before merging to continuously manage code quality.

# Q. As AI is being introduced, the role of developers is gradually changing. What does it mean to “do a good job” as a developer?

**Hyeokjin |** I think it means completing tasks within the expected timeframe and with a high level of completion. If a task is completed by the expected time and does not need to be revisited, that is doing a good job. It might sound basic, but sticking to the basics is what makes someone a good developer.

**Bokeum |** When working with others, everyone has different opinions. I think a good developer is someone who can quickly find the minimum consensus among different opinions and move the work forward. It is about having the ability to reach agreement, rather than getting stuck in endless discussions.

# Q. What kind of person will Backend.AI’s development team need in the future?
![A7206122.jpg](https://cdn.lablup.com/A7206122_3f52ebdb41.jpg)
**Bokeum |** Recently, a friend asked me what kind of developer Lablup is looking for, and I thought about it. In my opinion, Lablup wants a “social developer nerd.” Someone who has their own unique color but can also actively cooperate. It is not just about being technically strong. People who enjoy open source, developer culture, and developer communities would be a better fit.

**Hyeokjin |** The ability to refine various requirements and shape them into implementable forms is becoming increasingly important. With the advancement of AI tools, simple prototyping has become easier, but the ability to structure the whole system and analyze code is more important. When multiple contexts overlap, AI might take the work in the wrong direction. Ultimately, to build a maintainable system, it is crucial to clearly refine requirements and consistently accept various implementations based on those refined requirements. Context management and requirement refinement skills will become even more important in the future.

---

This interview was conducted in July 2025.

Interviewee: Hyeokjin Kim, Bokeum Kim

Interviewer, Editor, and Photographer: Jinho Heo

## Join Lablup!

[Go to Lablup Careers](https://www.lablup.com/ko/careers)

The journey to building a trusted Backend.AI

Building Backend.AI from its core | Hyeokjin Kim, Bokeum Kim

Jongeun Lee, Sujin Kim

A frontend world where questions matter more than definitive answers

Lablup is a company we ourselves feel is quite unique. Here, everyone works in the way that suits them best, anyone can freely ask questions and share opinions, and if something needs to be done, anyone can start it immediately regardless of seniority, title, or role. Behind the scenes at Lablup, you will find about 40 people united by the goal of making the complex environments of AI and machine learning easier to handle, even just a little.

We met with Jongeun Lee, the lead of the FrontendDev team, and Sujin Kim, a frontend engineer, who are transforming the challenging command-line interfaces for AI development and services into logical, easy-to-use, and beautiful screens, to hear about their world.

# Q. Please introduce yourselves.
 ![](https://cdn.lablup.com/A7205758_2_27ce77d4ad.jpg) 
**Jongeun**: Hello, I’m Jongeun Lee, leading the FrontendDev team at Lablup. My role involves organizing all UI-related project requirements needed by Backend.AI and managing their development priorities. To use a metaphor, I’m like a conductor who also occasionally plays an instrument.

**Sujin**: I am Sujin Kim, working under Jongeun’s leadership. I mainly develop the frontend for Backend.AI WebUI and the B2B enterprise resource management product Control Panel. I also handle frontend engineering for Lablup’s website and various sub-projects alongside my teammates.


# Q. How did you join Lablup?
**Jongeun**: I started my career as a web developer but once got tired of web development (laughs) and switched to app development. Through various developer community activities, I met people like Lablup’s CEO Jeongkyu Shin, CTO Joongi Kim, and Cloud Native team lead Manseok Cho. I also got to know Wooyoung Yoo, a DevRel in Lablup, through the JavaScript developer forum. Since Shin and Kim were active in TNF (Tatter Network Foundation) and I was involved in Creative Commons nonprofit activities, our paths crossed, which eventually led to my joining Lablup. Although the type of work changed, I felt I could trust these people and work well with them. Having trustworthy colleagues helped me overcome the challenge of moving from B2C to B2B.

**Sujin**: I joined Lablup as an intern during university through a Job Fair program. The people I met during the two-month internship left a great impression. I believe that people are more important than the work itself, and the atmosphere and personalities at Lablup fit me well. The company has a free-spirited environment where people don’t rush aimlessly but stay focused and passionate, which left a very positive memory.


# Q. What does the FrontendDev team do, and what languages or frameworks do you use?
**Jongeun**: We handle frontend development for various projects such as Backend.AI WebUI, the enterprise resource management service Control Panel, the MLOps platform FastTrack 2, and the inference launcher PALI. Since we don’t have dedicated UI/UX designers, our team also covers a certain amount of UI and UX work. Our role is to leverage frontend technologies to make products easier and more comfortable to use for customers of Backend.AI and related products.

**Sujin**: We mainly use TypeScript and React. In the past, we used Lit for Web Components, but after Jongeun joined, we decided to migrate to React, and most components have already been redeveloped in React. We also actively use GraphQL and develop with Relay, one of the main GraphQL clients.

# Q. Does the FrontendDev team have any unique development philosophies?
 ![](https://cdn.lablup.com/A7205786_1_823407486f.jpg) 

**Jongeun**: Our philosophy closely relates to React Relay and GraphQL, and the key keyword is composability. As projects scale, composability can suffer, or even if the end result is highly composable, building each component might not be developer-friendly. Backend.AI has a history of nearly 10 years, so components often need to be designed for long-term use. 

Since we don’t have UI/UX designers, we must maintain consistency in user experience through reusable, composable units. Unlike most frontend teams that receive polished UI/UX designs and requirements from designers and planners, we often start without those and communicate through “code storytelling.” We rapidly prototype visual concepts and discuss based on those prototypes, then build the pipeline from there.

# Q. Securing composability must have been challenging. How was the process?
**Sujin**: It took some time to get used to. I had habits from using Web Components, but because React and Web Components have different approaches, we had to pay attention to things like which data parents should hold and what data should be passed to children components. We put a lot of effort into reaching consensus on data flow within the team, and once aligned, overall state management became easier, which was very rewarding.

We also increasingly use AI internally, and figuring out how to prompt AI properly or monitor tasks to achieve our goals accurately is another challenge. We need to understand well the tasks we assign AI to properly identify issues and implement solutions quickly.

# Q. The AI coding era is here, or at least arriving. What are your thoughts?
 ![](https://cdn.lablup.com/A7205854_1_53ac67f661.jpg) 

**Jongeun**: I believe frontend development as a job will continue to exist. However, the form it takes may change — from GUI towards voice-based interfaces and beyond. Since voice has its limitations, I think we will move toward multi-UI approaches. Because frontend development deals directly with user interaction, predicting future trends is challenging. There will be a greater need for supervisors who manage this diversity and unify it into coherent experiences, so frontend roles will gradually shift in that direction.

**Sujin**: Recently, we held a [Lablup Idea | up internal hackathon](https://www.backend.ai/ko/blog/2025-07-1st-lablup-ai-hackathon) using Claude Code to prototype products quickly. It didn’t take much time to create an app with a nice interface and animations. However, the AI-generated code is not perfect — sometimes the existing APIs don’t fit the needed data format or functionality. Then, knowing the structure well allows you to decide whether to improve AI’s output or rebuild it from scratch. There’s still a lot of human input needed, and going forward, good direction will be required from skilled frontend developers.

**Jongeun**: To add, when we instruct AI, we need to think about planning aspects well — it’s not just about generating a landing page but about considering UI and UX deeply. This means knowing how to identify problems and requiring a higher level of frontend knowledge. Traditionally, frontend links planning and core development, but AI’s arrival only emphasizes this role more.

# Q. Jongeun, you work remotely from overseas. How do you manage your work?
**Jongeun**: Remote work lacks face-to-face interaction, so more care is needed for both productivity and relationship building. Our team actively uses sync meetings. Early on, these were almost daily, but now with collaboration tools like Jira, we meet three times a week to share progress and discuss any blockers, facilitating collaboration. We were the first team at Lablup to introduce one-on-one meetings to address difficulties and improvements so team members can focus on work. I visit Korea once or twice a year and try to work from the office when I do.

# Q. Are there activities to strengthen bonds among team members?
 ![](https://cdn.lablup.com/2025_07_08_235637_a6907a86c0.png) 
**Sujin**: We hold 'DevTalk' sessions twice a month, where team members share interesting tech news or updates. In remote work, casual info sharing is rare, so this helps fill that gap.
 ![](https://cdn.lablup.com/2025_07_08_235741_2700fae69d.png) 
**Jongeun**: Company-wide, we have ‘Coffee Chat’ every Friday for 30-45 minutes, where everyone meets online to talk about non-work topics. I pushed to keep this culture going since I joined. We also sometimes gather after lunch to discuss tech or hobbies, helping build closeness if people want.

# Q. What parts of work are closely linked to personal growth?
**Sujin**: A unique aspect is that developers participate in exhibitions. Our team meets potential customers, hears their feedback, and thinks about how to present our product externally and improve based on their input, often with support from our DevRel team.

**Jongeun**: Developers feel happiest when they receive feedback on their products. In B2C, feedback is immediate, letting you see improvements clearly. In B2B, customers tend to be quiet unless big issues arise, so exhibitions are valuable opportunities to get direct feedback and find clues for improvement.

**Sujin**: There are many opportunities to present, from our annual lab | up > /conf to external events like PyCon. The company encourages and supports presentations, including in open-source activities, which helps build soft skills as well. (laughs)


**Jongeun**: We also have a retrospective system for organizational and personal growth, often using KPT (Keep, Problem, Try) every one or two months. It helps us reflect on what to maintain or change, and set future directions — we actively encourage participation.

# Q. What do you think is Lablup’s biggest attraction?
 ![](https://cdn.lablup.com/A7205815_1_420a08e517.jpg) 
**Sujin**: Two things stand out — first, passionate and unique people working together; second, our technological strength. Before joining, I researched online and felt that the company has clear vision and good products. Upon joining, it was obvious where the technology strength comes from. Everyone gathers to do what they love, and there is no one dismantling the organization — everyone is united toward the company’s goals, which I treasure.

# Q. What moment have you felt the most rewarding?
**Jongeun**: I feel a big sense of responsibility as a team leader. About a year after joining, I was walking with team members after lunch near the office when they told me, “We are so glad you joined. The team feels very different before and after you came.” I was going through a period of self-doubt about whether I was fulfilling my role well due to domain and responsibility changes, so their words reassured me and made me proud that I was doing well.

**Sujin**: I feel proud when our team is recognized. At exhibitions, many visit other booths but recognize us by our badges and say, “We are watching Lablup and learning from you.” That makes me happiest — it tells me we are on the right track.

# Q. What kind of talents would you like to work with?
 ![](https://cdn.lablup.com/A7205852_1_dcd4e67aa2.jpg) 
**Sujin**: Development is a team effort, so we want people who communicate easily, are not afraid to express opinions or ask questions. At Lablup, we have a system where questions are posted publicly in Teams, and anyone can answer. What you wonder might also be what others are curious about, so this builds a valuable shared resource. It can feel strange or uncomfortable at first, but with experience, it proves to be very efficient. Also, many people here love exercising, so it would be great if someone joins us who enjoys working out too! (laughs)

**Jongeun**: Fit with the company matters. Lablup’s fit is clear: people who can find work on their own initiative and solve problems. Not just people interested in code or technology, but those who think deeply about what ultimate UX our code delivers to users — those people will harmonize well with us.

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Interviewees: Jongeun Lee, Sujin Kim

Interviewer, Editor, Photographer: Jinho Heo


### Join Lablup!
[Go to Lablup Careers](https://www.lablup.com/careers)

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