VLA/Robot Learning Engineer

Vision-Language-Action(VLA) 및 imitation learning·강화학습 기반의 manipulation policy를 개발하여, 실제 로봇의 task 성공률과 일반화(generalization) 성능을 지속적으로 끌어올리는 역할입니다. 다양한 작업과 환경에서 로봇이 스스로 잘 동작하도록 만드는, AI Core의 핵심 학습 축을 담당합니다. In this role you build manipulation policies based on Vision-Language-Action (VLA), imitation learning, and reinforcement learning — continuously improving real-robot task success and generalization.

You own a core learning axis of AI Core: making robots perform reliably across diverse tasks and environments. 주요업무 (Key Responsibility) VLA·imitation learning·RL 기반 manipulation policy 개발 실 로봇의 task success rate 및 generalization 성능 측정·개선 대규모 demonstration 데이터를 활용한 policy 학습 파이프라인 구축 다양한 task와 환경 일반화를 위한 모델 구조·학습 전략 실험 AI Core·데이터·로봇 OS 팀과 협업하여 학습된 policy를 실 배포로 연결 Develop manipulation policies based on VLA, imitation learning, and RL Measure and improve real-robot task success rate and generalization Build policy training pipelines that leverage large-scale demonstration data Experiment with model architectures and training strategies for cross-task/environment generalization Collaborate with AI Core, Data, and Robot OS teams to move trained policies into deployment Requirements Robot learning, manipulation policy, 또는 VLA 관련 3~5년의 연구·개발 경험 Imitation learning, reinforcement learning, 또는 foundation model 기반 policy 학습 경험 Python·Py.

Torch 기반 대규모 모델 학습 및 실험 역량 실 로봇 또는 시뮬레이터에서 policy를 학습·평가해본 경험 컴퓨터공학·AI·로봇공학 관련 석사 이상 또는 그에 준하는 경험 3–5 years of research/engineering experience in robot learning, manipulation policy, or VLA Experience with imitation learning, reinforcement learning, or foundation-model-based policy learning Strong large-scale model training skills in Python and Py.

Torch Experience training and evaluating policies on real robots or simulators Master's degree in CS, AI, Robotics, or equivalent experience Preferred VLA, robot foundation model(π0, NVIDIA Gr00t 등), large-scale imitation learning 관련 연구 실적 대규모 로봇 demonstration 데이터셋 구축·활용 경험 Sim-to-real 및 multi-task generalization 경험 로봇공학·AI 석/박사 학위 실제 제품·현장에 학습 policy를 배포해본 경험 Research track record in VLA, robot foundation models (π0, NVIDIA Gr00t, etc.)

, or large-scale imitation learning Experience building/using large robot demonstration datasets Sim-to-real and multi-task generalization experience Master/PhD in Robotics or AI Experience deploying learned policies into products or the field 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)