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ARAI Robot Association logo

AI Robot Association

Heiwajima

R&D-029 AI Engineer (VLAs).

FULL TIMEDATA & ML

※日本語版が続きます。

About AIRoA

The AI Robot Association (AIRoA) is launching a groundbreaking initiative: collecting one million hours of humanoid robot operation data with hundreds of robots, and leveraging it to train the world’s most powerful Vision-Language-Action (VLA) models.

What makes AIRoA unique is not only the unprecedented scale of real-world data and humanoid platforms, but also our commitment to making everything open and accessible. We are building a shared “robot data ecosystem” where datasets, trained models, and benchmarks are available to everyone. Researchers around the world will be able to evaluate their models on standardized humanoid robots through our open evaluation platform.

Job Description

  • Develop Vision-Language models, or equivalent multimodal models, with a view toward applications in the robotics domain
  • Fine-tune existing models, conduct evaluations, perform error analysis, and improve performance
  • Build training pipelines using real-world data, design evaluation metrics, and operate iterative improvement cycles
  • Prepare and preprocess data, and build training environments for image, video, language, and action data
  • Research the latest trends in technologies and academic studies, select appropriate technologies, and incorporate findings into model improvements
  • Establish training infrastructure, inference infrastructure, and experimental environments for real-world model deployment
  • Collaborate with related teams such as software engineers and robotics engineers to define requirements, design validation plans, and drive development

AIRoAについて

AI Robot Association(AIRoA)は、画期的な取り組みを開始します。数百台のヒューマノイドロボットを用いて、ヒューマノイドロボットの操作データを100万時間分収集し、それを活用してVision-Language-Action(VLA)モデルを学習させます。

私たちの独自性は、実世界データとヒューマノイドプラットフォームの前例のない規模だけではなく、あらゆるものをオープンでアクセス可能にするというコミットメントにもあります。AIRoAはデータセット、学習済みモデル、ベンチマークを誰もが利用できる共有の「ロボット・データ・エコシステム」の構築を目指しています。実現が成功すれば、世界中の研究者が、私たちのオープン評価プラットフォームを通じて、標準化されたヒューマノイドロボット上で自らのモデルを評価できるようになることを期待しています。

業務内容

  • ロボティクス領域への応用を見据えた Vision-Languageモデル、またはそれに準ずるマルチモーダルモデルの開発
  • 既存モデルの fine-tuning、評価、エラー分析、性能改善
  • 実データを用いた学習パイプライン構築、評価指標設計、改善サイクル運用
  • 画像・動画・言語・行動データ等を対象としたデータ整備、前処理、学習環境構築
  • 最新の研究・技術動向の調査、技術選定、およびモデル改善への反映
  • モデルの実運用を見据えた学習基盤・推論基盤・実験環境の整備
  • ソフトウェアエンジニア、ロボティクスエンジニア等の関連チームと連携した要件整理、検証設計、開発推進

Requirements

※日本語版が続きます。

Required Qualifications

  • Experience leading machine learning models from deployment to improvement and operation in a production service environment
  • Experience implementing, training, and evaluating machine learning models using Python and PyTorch
  • Hands-on experience fine-tuning Vision-Language models, or equivalent multimodal models
  • Experience building training pipelines with real-world data, designing evaluations, conducting error analysis, and operating improvement loops
  • Ability to understand the latest research and technology trends and translate them into model improvements and practical product applications

Preferred Qualifications

  • Experience developing Vision-Language-Action (VLA) models or multimodal models for robotics
  • Experience with robot control, ROS / ROS 2, C++, and real-world hardware evaluation
  • Knowledge of or experience in sensor integration, actuator control, action generation, and low-level control
  • Familiarity with training and evaluation using simulators, Sim2Real, and domain adaptation
  • Experience building training and inference infrastructure in cloud environments such as AWS or GCP
  • Experience with reproducible and operationally robust development practices such as Docker, CI/CD, and MLOps

必須要件

  • 実サービス環境において、機械学習モデルの導入から改善・運用まで携わった経験
  • Python / PyTorch を用いた MLモデルの実装・学習・評価 の経験
  • Vision-Languageモデル、またはそれに準ずるマルチモーダルモデル の fine-tuning の実務経験
  • 実データを用いた学習パイプライン構築、評価設計、エラー分析、改善ループ運用の経験
  • 最新の研究・技術動向を理解し、モデル改善や実プロダクトへの応用に落とし込める能力

歓迎要件

  • Vision-Language-Action(VLA)モデル、またはロボティクス向けマルチモーダルモデルの開発経験
  • ロボット制御、ROS / ROS 2、C++、実機評価の経験
  • センサ統合、アクチュエータ制御、行動生成、低レベル制御に関する知識または経験
  • シミュレータを用いた学習・評価、Sim2Real、Domain Adaptationなどに関する知見
  • AWS / GCP 等のクラウド環境を用いた学習・推論基盤の構築経験
  • Docker、CI/CD、MLOps など再現性・運用性を意識した開発経験

Benefits

●Work location

Tokyo Ryutsu Center A Bldg. AW4-5/4-6, 6-1-1 Heiwajima, Ota-ku, Tokyo 143-0006, Japan

Pay
Not stated
Where
Heiwajima · Tokyo · Japan
Work
Not stated
Type
Full time
Posted
10 Apr, 2026
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