{"id":922945,"url":"https://alion.io/job/laplacian-cross-embodiment-learning-engineer","title":"Cross-Embodiment Learning Engineer","company":{"id":681600,"name":"Laplacian","domain":"laplacian.cc","url":"https://alion.io/company/laplacian","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seongnam, South Korea"],"countries":["KR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":33000,"max_usd":95000,"period":"year","method":null,"sample_n":9274},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ChatGPT","optional":false},{"name":"Claude","optional":false},{"name":"Google Workspace","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Linear","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Slack","optional":false},{"name":"Transfer Learning","optional":false}],"status":"live","first_seen_at":"2026-08-21T00:00:00Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-09-29T15:23:46Z","board_verified":true,"closed_at":null,"days_open":40,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":40},"description":"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.\nIn 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.\n주요업무 (Key Responsibility)\n서로 다른 embodiment(robot·arm·gripper)의 state/action을 공통 표현으로 정규화\nUMI, ego-centric video 등 robot-free human demonstration 데이터의 통합·활용\n하나의 policy·데이터를 다양한 embodiment로 전이(transfer)하는 학습 기법 개발\nCross-embodiment 일반화 성능을 정량 평가하는 벤치마크 설계\nNormalize states/actions across different embodiments (robots, arms, grippers) into a shared representation\nIntegrate and leverage robot-free human demonstration data (UMI, ego-centric video, etc.)\nDevelop learning methods that transfer a single policy/dataset across diverse embodiments\nDesign benchmarks that quantify cross-embodiment generalization\nRequirements\nCross-embodiment learning, transfer learning, 또는 robot manipulation 관련 3~5년의 연구·개발 경험\nImitation learning, representation learning, 또는 multi-embodiment policy 학습 경험\nPython·PyTorch 기반 모델 학습 역량\n다양한 로봇 형태 또는 demonstration 데이터를 다뤄본 경험\n컴퓨터공학·AI·로봇공학 관련 석사 이상 또는 그에 준하는 경험\n3-5 years of research/engineering experience in cross-embodiment learning, transfer learning, or robot manipulation\nExperience with imitation learning, representation learning, or multi-embodiment policy learning\nStrong model training skills in Python and PyTorch\nExperience working with diverse robot form factors or demonstration data\nMaster's degree in CS, AI, Robotics, or equivalent experience\n우대사항(Preferred)\nCross-embodiment learning, UMI, ego-centric video 기반 학습 관련 연구 실적\nRepresentation learning 또는 domain adaptation 경험\n다양한 robot·gripper 하드웨어를 다뤄본 경험\n로봇공학·AI 석/박사 학위\n대규모 heterogeneous 로봇 데이터셋 구축·활용 경험\nResearch track record in cross-embodiment learning, UMI, or ego-centric video based learning\nExperience with representation learning or domain adaptation\nExperience with diverse robot/gripper hardware\nMaster/PhD in Robotics or AI\nExperience building/using large heterogeneous robot datasets\nBenefits\nClaude & ChatGPT subscriptions provided (Claude · ChatGPT 유료 플랜 지원)\nMinimal meetings with fast decision-making (불필요한 회의를 최소화하고 빠르게 의사결정합니다.)\nModern intranet/tools (Google Workspace, Slack, Notion, Linear, Workable, Flex.team 등)","description_format":"text","description_chars":2645,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Industrial Real Estate"],"lifecycle":[{"event":"open","at":"2026-09-15T02:59:25Z"}],"liveness":{"score":22,"band":"cold","label":"Long shot","p_open":1,"p_active":0.486,"p_room":0.45,"age_days":39,"expected_fill_days":21,"reasons":["conf:5","win:tail"],"computed_at":"2026-09-29T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/laplacian-cross-embodiment-learning-engineer","json_url":"https://alion.io/job/laplacian-cross-embodiment-learning-engineer.json","meta":{"generated_at":"2026-09-30T03:42:06Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2586,"day_limit":5000,"remaining_today":2414,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}