Senior Research Engineer, Embodied Generalist Agent, Gaming

掲載中・1日前に確認

給与: 記載なし
勤務地: 東京都
休日: 記載なし
雇用形態: 記載なし

どんな仕事かAIによる生成

Develop and optimize embodied gaming agents that integrate multimodal perception, reasoning, planning, and real-time execution. Build training recipes and systems for long-horizon reasoning, memory use, adaptation to new environments, and benchmarking general agent capabilities.

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応募資格

必須

Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in one or more programming languages.
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
Experience with ML frameworks such as JAX, TensorFlow, or PyTorch.
Google はグローバル企業であり、効率的なコラボレーションとコミュニケーションを全世界で促進するために、求人情報に別段の記載がない限り、英語に習熟していることがすべての職種で必須とされています。

歓迎

Master's degree or PhD in Computer Science or related technical field.
Experience building agents for 3D virtual environments, simulators, or video games.
Domain knowledge in game development, concepting, or community.
Solid understanding of LLM internals, (e.g., typical training pipelines, computational characteristics of training/inference, etc.), and knowledge of Deep Reinforcement Learning (RL), LLM Reasoning, imitation learning, memory-based architectures, Vision -Language-Model (VLM), or Vision -Language-Action (VLA) models.
Proven track record of designing, implementing, and maintaining robust technical assets (such as shared libraries, frameworks, or models) for technical stakeholders across collaborative research and engineering environments and experience with OSS contributions.

求める人物像

highly motivated and innovative
If you are passionate about pushing the frontiers of what AI agents can achieve and are eager to define the next era of adaptive intelligence

募集要項最終確認 10/21

職種: 機械学習エンジニア
雇用形態: 記載なし
役職: Senior Research Engineer
給与: 記載なし
勤務地: 東京都Tokyo
働き方: 記載なし
経験年数: 5年以上
学歴: 大学卒業以上
掲載日: 2026/10/12 06:35（10日前）
最終確認: 2026/10/21 22:01（1日前）

仕事内容

この求人について

DeepMind has long-established connections with gaming, whether through early AI-breakthroughs mastering Atari, Go, StarCraft, and more recent research with SIMA agents. We are seeking a highly motivated and innovative Research Engineer to join our team in Tokyo, focused on building multimodal embodied gaming agents.
In this role, you will work with researchers and engineers to develop embodied gaming agents capable of perceiving, reasoning, planning, and executing precise real-time actions in complex, open-ended environments. You will utilize the latest advancements in multimodal large language models (LLMs), vision-language-action (VLA) models, in-context learning (ICL), supervised fine-tuning (SFT), and reinforcement learning (RL) to solve fundamental challenges in embodied intelligence. You will leverage these architectures to bridge the gap between high-level long-horizon planning and low-level high-frequency motor control, creating agents that can adaptively master tasks in rich virtual testbeds (including high-fidelity 3D simulations and sandbox games).
This role offers you a unique opportunity to stand at the forefront of the quest for AI. You will join an exceptional team solving the "hard problems" of embodiment—autonomously solving long-horizon tasks, learning from vast multimodal memories, and generalizing to completely unseen worlds. If you are passionate about pushing the frontiers of what AI agents can achieve and are eager to define the next era of adaptive intelligence, we encourage you to apply.

仕事の内容

Develop and optimize agent architectures that seamlessly integrate multimodal perception, reasoning, and precise real-time execution.
Build and scale training recipes utilizing supervised fine-tuning, reinforcement learning, imitation learning, or in-context learning.
Design advanced systems that enable agents to reason over long horizons and effectively utilize memory to solve complex, extended tasks.
Research and implement capabilities that allow agents to adapt to new environments and learn from experience at test time.
Establish rigorous benchmarks within virtual environments to measure progress in general agent capabilities and embodied intelligence in unseen environments.

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