{"id":1200866,"url":"https://alion.io/job/reazon-rdresearch-scientist-aifoundation-models","title":"[R&D]Research Scientist - AI/Foundation models","company":{"id":2289494,"name":"Reazon Holdings","domain":"reazon.jp","url":"https://alion.io/company/reazon","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Talentio","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Japan"],"countries":["JP"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Linux","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-09-24T20:46:47Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-19T16:45:02Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":24},"description":"About Enactic\nBuilding humanoids that learn to care, for facilities and homes worldwide.\nEnactic is a deeptech startup building assistive humanoid robots that support and enrich daily life. As aging demographics accelerate and labor shortages grow, we deploy robots where help is needed most: in care facilities and homes.\n---\n「世界中の家庭や施設に生活支援ヒューマノイドを普及させる」\nEnactic は、支援が必要な人の自立性と生活の質（QOL）を高めるヒューマノイドロボットを開発・提供することを使命とするディープテック企業です。\n高齢化による深刻な労働力不足という世界的課題に直面する中で、私たちはその解決に貢献することを目指しています。\nResponsibilities\nAs our AI and Foundation Model Research Scientist, you will build Physical AI systems for humanoids that learn to care for facilities and homes. \nYour mission is to establish a continuous improvement cycle between real-world data collection and perception-decision-action models, creating AI that's safe, practical, adaptive, dexterous, and robust.\n---\nEnacticのAI・基盤モデルリサーチサイエンティストとして、施設や家庭で生活支援を行うヒューマノイドのためのフィジカルAIシステムを構築していただきます。\nあなたのミッションは、実世界のデータ収集と「認知・判断・行動」モデル間の継続的な改善サイクルを確立し、安全で実用的、適応性が高く、巧みな操作と堅牢性を兼ね備えたAIを創出することです。\nRequirements\n・Experience building AI systems that perform in real-world environments through experimentation with world models or foundation models\n・Ability to lead the design and validation of continuous improvement cycles: real-world data → model → action → feedback\n・Experience developing and operating production-quality libraries and systems in C/C++, Python, Docker, Linux, and Git\n---\n・ワールドモデルや基盤モデルを用いた実験を通じ、実環境で機能するAIシステムを構築した経験\n・「実世界データ → モデル → 行動 → フィードバック」という継続的改善サイクルの設計および検証を主導・牽引する能力\n・C/C++、Python、Docker、Linux、Gitを用いたプロダクションクオリティのライブラリやシステムの開発・運用経験\nBonus Qualifications\n・Experience implementing force control, compliance control, and bilateral control in real-world environments\n・Experience creating or making significant contributions to widely-used open-source projects\n・Experience designing large-scale multimodal data pipelines for acquisition, synchronization, and preprocessing\n・Experience designing algorithms for behavior models, such as stable learning, safe exploration, and online adaptive control\n・Experience bridging technology development and social acceptance through design and real-world implementation\n・Experience with distributed training, optimization, and hyperparameter tuning of billion-parameter-scale deep learning models\n・Understanding of theory and implementation for cutting-edge methods including representation learning, world models, imitation learning, and reinforcement learning\n・Deep understanding of humanoid mechanics, actuator control, and sensor integration\n・Experience developing systems applying deep learning to whole-body motion control, locomotion, and manipulation\n・Strong communication skills to drive cross-disciplinary collaboration\n---\n・実環境における力制御、コンプライアンス制御、バイラテラル制御の実装経験\n・広く利用されているオープンソースプロジェクトの立ち上げ、または主要メンバーとしての貢献経験\n・データ取得、同期、前処理のための大規模マルチモーダルデータパイプラインの設計経験\n・安定学習、安全な探査、オンライン適応制御など、行動モデルのためのアルゴリズム設計経験\n・デザインや実環境への実装を通じて、技術開発と社会的受容性の架け橋となった経験\n・数十億〜数千億パラメータ規模の深層学習モデルにおける分散学習、最適化、ハイパーパラメータチューニングの経験\n・表現学習、ワールドモデル、模倣学習、強化学習などの最先端手法に関する理論の理解および実装経験\n・ヒューマノイドの機構、アクチュエータ制御、センサ統合に関する深い理解\n・全身運動制御、移動、マニピュレーションへ深層学習を適用したシステム開発経験\n・専門領域を超えたコラボレーションを強力に推進するための高いコミュニケーション能力","description_format":"text","description_chars":3159,"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":[{"language":"Japanese","level":"Advanced (C1)","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial 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