AlgorithmLabs 엔지니어링 문화: 우리가 일하는 방식

A question I get in every client meeting

"The tech looks great, but how big is your team?"

Over the past three years and 500+ client meetings, I've heard this question almost every time. Enterprise IT decision-makers in particular always want to confirm: "Can this company handle a project at our scale?" We're not a big corporation. But the answer to this question isn't simply headcount — our actual project outcomes prove that.

How a small team delivers on enterprise projects

AlgorithmLabs is built around a small group of senior engineers. People often frame it as "elite team," but the real reason we operate that way is that each team member covers an unusually wide scope.

At most companies, frontend engineers do frontend and backend engineers do backend. On our team, one person handles everything from frontend to infrastructure deployment. This works because our hiring criterion isn't "proficiency in a specific technology" but "ability to learn new problems quickly and solve them." Last quarter, a KB Financial project required a full architecture redesign within two weeks due to client-side infrastructure changes. At another company, this would involve infra, backend, and QA teams moving sequentially — easily a two-month effort. Our team of three focused and completed the migration in nine days.

Why decisions happen fast

Tech selection criteria: not "newest" but "does it solve the customer's problem?"

For tech stack decisions, we follow one principle: "Can this be deployed in the customer's environment?" A technology that doesn't run in the customer's setup is meaningless, no matter how impressive.

Current stack:

• Frontend: Next.js + TypeScript + Tailwind CSS
• Backend: Python + FastAPI
• AI/ML: PyTorch, LangChain, custom orchestration pipeline
• Infrastructure: AWS, Docker, Kubernetes

The short version of why: "Can this deploy in an enterprise On-premise environment?" was the #1 criterion. We chose Kubernetes specifically to reduce communication overhead with client infrastructure teams.

Deployment speed

PR review → automated tests → staging verification → production deployment: average under 4 hours. Daily deployments average 3–5. Being able to ship same-day hotfixes when client issues arise is an enormous competitive advantage in the field.

In client meetings, when someone asks "What's your response time for issues?" and I answer "Same-day deployment," most are surprised. The typical RFP SLA requirement is "patch within 72 hours" — we already far exceed that standard.

How our team works

• Async communication first — Cut unnecessary meetings; important decisions go into documents. Fewer meetings actually frees up more time for client response.
• Documentation culture — Every technical decision, client requirement, and incident response is recorded. New team members can get up to speed on history quickly.
• Code review required — Every PR gets at least one review. Code touching client data requires two.
• Weekly demo — Every Friday, everyone presents their week's work. This is where the most feedback flows and next week's priorities get set.

We're looking for people to join us

AlgorithmLabs is one of the fastest-growing teams in the enterprise AI space. We closed our Series B in 2025 and work with Korea's largest companies including KB Financial, LG, and Hyundai Motor.

If you've felt limited by a narrow role at a big company and want to grow fast by owning wide scope on a small team, we might be a great fit. You don't need a specific position in mind — let's just talk.

Hiring inquiries →