VLA/Robot Learning Engineer

한국어 공고는 아래에 있습니다.

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.

Key Responsibility

• 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

• 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 PyTorch
• Experience training and evaluating policies on real robots or simulators
• Master's degree in CS, AI, Robotics, or equivalent experience

Preferred

• 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

• No restrictions on the use of AI tools (Claude).
• We minimize unnecessary meetings and make decisions quickly.
• Modern intranet/tools — Google Workspace, Slack, Notion, Linear, Workable, Flex.team, etc.

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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.

주요업무

• 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

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·PyTorch 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

우대사항

• 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

Benefits

• Claude 등 AI 도구 사용에 제한이 없습니다.
• 불필요한 회의를 최소화하고 빠르게 의사결정합니다.
• Modern intranet/tools - Google Workspace, Slack, Notion, Linear, Workable, Flex.team 등.