Machine Intelligence AI Engineer

Laplacian Way

우리는

성남에 본사를 둔 AI 스타트업이다. 글로벌 시장을 겨냥한 최전선의 제품을 만든다. 소수 정예. 평범함은 용납하지 않는다. 빠르게 움직인다.

일하는 방식

실력은 기본이다. 진짜 차이는 그 다음에서 갈린다.

제품이 북극성이다. 어디로 갈지, 무엇을 만들지, 성공을 어떻게 정의할지 — 모두 제품에서 비롯된다. 기술을 위한 기술은 만들지 않는다. 실제 고객 현장에서 돌아가고, 실제 비즈니스 KPI를 움직이는, 처음부터 끝까지 작동하는 시스템. 우리가 존재하는 이유다.

기술은 제품을 가능하게 만드는 토대다. 제조와 물류를 완전 자율화하는 것 — 그것이 우리가 이기는 방식이다. 엔지니어링이 무게중심이며, 나머지 모든 것은 엔지니어링을 받치기 위해 존재한다.

사람이 기술과 비즈니스를 현실로 만든다. 그래서 사람이 먼저다. 우리가 함께하는 사람은 자기 역할이나 직책에 선을 긋지 않는다. 일을 스스로 찾아내고, 빈 곳을 메우고, 끝까지 해낸다. 모호함은 멈출 이유가 아니라 움직일 여지다. 문제는 그냥 실행하는 사람이 푼다.

우리는 자기보다 제품을 앞세우는 사람과 일한다. 역할, 권한, 조직의 형태를 바꾸는 것이 제품의 성공에 도움이 된다면, 기꺼이 바꾸는 사람. 사내 정치도, 자기 조직 키우기도 없다. 오직 제품의 성공을 위한 결정만 있을 뿐이다. 그것이 우리가 찾는 사람이다.

속도, 속도, 속도. 내놓고, 배우고, 다시 내놓는다 — 안주할 틈 없이 빠르게.

About Us

Headquartered in Seongnam, we're an AI startup building frontier products for global markets. Small team, zero tolerance for mediocrity, moving fast.

How We Work

Skill is the bare minimum. What sets us apart is what comes next.

Product is our north star. Where we're going, what we build, how we define success — all of it flows from the product. Not tech for tech's sake. End-to-end systems that run on a real customer floor and move real business KPIs. That's why we exist.

Technology is what makes the product possible. Making manufacturing and logistics fully autonomous is how we win. Engineering is the center of gravity — everything else exists to serve it.

People are what make the technology and the business real. That's why people come first. The people we hire don't draw lines around their scope or their title. They find the work, close the gaps, and see it through. Ambiguity is room to operate, not a reason to stall. Problems get solved by people who just execute.

We hire people who put the product ahead of ego, title, and turf. If changing their role, ownership, or team structure helps the product win, they change it. No politics. No empire-building. Just what makes the product win.

Speed, speed, speed. We ship, we learn, we ship again — faster than feels comfortable.

About the Role

We are seeking a Machine Intelligence AI Engineer to design and develop intelligent systems that enable robots to perceive, reason about, and interact with the physical world. You will work at the intersection of foundation models, simulation, and real-world robotics to build the "brain" that powers industrial manipulators and humanoid robots.

Key Responsibilities

• Design and implement end-to-end Physical AI pipelines that map raw sensor inputs (RGB-D, force/torque, proprioception) to robot actions for manipulation and locomotion tasks
• Develop and fine-tune foundation models (vision-language-action models, diffusion policies, world models) for robot control of industrial manipulators and humanoid platforms
• Build high-fidelity simulation environments (Isaac Sim, MuJoCo, or equivalent) and implement sim-to-real transfer strategies including domain randomization and system identification
• Architect scalable data pipelines for collecting, curating, and labeling large-scale robot demonstration datasets (teleoperation, human video, synthetic data)
• Collaborate with mechanical and electrical engineering teams to co-design hardware-software interfaces, sensor suites, and actuator control architectures
• Establish evaluation frameworks, benchmarks, and safety protocols for physical AI systems operating in unstructured environments

Requirements

• M.S. or Ph.D. in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or a closely related field
• 3+ years of hands-on experience developing AI/ML systems for physical robots (manipulators, mobile robots, or humanoids)
• Strong proficiency in Python with experience in at least two of: PyTorch, JAX, TensorFlow for training large-scale models
• Demonstrated expertise in robot learning methods: imitation learning, reinforcement learning, or visuomotor policy learning
• Proven track record of deploying learned policies on physical robot hardware in lab or production settings
• Experience with vision-language-action (VLA) models, diffusion-based policies, or world models for robot control

Preferred Qualifications

• Strong publication record or equivalent industry experience in robotics, computer vision, or machine learning
• Knowledge of NVIDIA Isaac platform ecosystem (Isaac Lab, Isaac Manipulator, Omniverse) or equivalent toolchains
• Experience optimizing ML models for edge deployment using TensorRT, ONNX Runtime, o...