Cross-Embodiment Learning Engineer

서로 다른 로봇·arm·gripper의 state/action을 공통 표현으로 정규화하고, UMI(Universal Manipulation Interface), ego-centric video 등 robot-free human demonstration 데이터까지 통합 활용하여, 하나의 policy와 데이터를 다양한 embodiment로 전이, 학습할 수 있는 기술을 개발하는 역할입니다. '한 번 배운 것을 모든 로봇으로 확장'하는, 스택의 확장성을 책임집니다.

In this role, you normalize the states/actions of different robots, arms, and grippers into a shared representation and integrate robot-free human demonstration data (UMI, ego-centric video, and more) so that a single policy and dataset can transfer and learn across diverse embodiments and also own the scalability of our stack-learn once, extend to every robot.

주요업무 (Key Responsibility)

• 서로 다른 embodiment(robot·arm·gripper)의 state/action을 공통 표현으로 정규화
• UMI, ego-centric video 등 robot-free human demonstration 데이터의 통합·활용
• 하나의 policy·데이터를 다양한 embodiment로 전이(transfer)하는 학습 기법 개발
• Cross-embodiment 일반화 성능을 정량 평가하는 벤치마크 설계
• Normalize states/actions across different embodiments (robots, arms, grippers) into a shared representation
• Integrate and leverage robot-free human demonstration data (UMI, ego-centric video, etc.)
• Develop learning methods that transfer a single policy/dataset across diverse embodiments
• Design benchmarks that quantify cross-embodiment generalization

Requirements

• Cross-embodiment learning, transfer learning, 또는 robot manipulation 관련 3~5년의 연구·개발 경험
• Imitation learning, representation learning, 또는 multi-embodiment policy 학습 경험
• Python·PyTorch 기반 모델 학습 역량
• 다양한 로봇 형태 또는 demonstration 데이터를 다뤄본 경험
• 컴퓨터공학·AI·로봇공학 관련 석사 이상 또는 그에 준하는 경험
• 3-5 years of research/engineering experience in cross-embodiment learning, transfer learning, or robot manipulation
• Experience with imitation learning, representation learning, or multi-embodiment policy learning
• Strong model training skills in Python and PyTorch
• Experience working with diverse robot form factors or demonstration data
• Master's degree in CS, AI, Robotics, or equivalent experience

우대사항(Preferred)

• Cross-embodiment learning, UMI, ego-centric video 기반 학습 관련 연구 실적
• Representation learning 또는 domain adaptation 경험
• 다양한 robot·gripper 하드웨어를 다뤄본 경험
• 로봇공학·AI 석/박사 학위
• 대규모 heterogeneous 로봇 데이터셋 구축·활용 경험
• Research track record in cross-embodiment learning, UMI, or ego-centric video based learning
• Experience with representation learning or domain adaptation
• Experience with diverse robot/gripper hardware
• Master/PhD in Robotics or AI
• Experience building/using large heterogeneous robot datasets

Benefits

• Claude & ChatGPT subscriptions provided (Claude · ChatGPT 유료 플랜 지원)
• Minimal meetings with fast decision-making (불필요한 회의를 최소화하고 빠르게 의사결정합니다.)
• Modern intranet/tools (Google Workspace, Slack, Notion, Linear, Workable, Flex.team 등)