POWERD LAB の求人一覧 - 株式会社パワーエックス

■Engineering & Research Division / Full Stack Engineer ■Summary We are looking for an experienced Fullstack Software Engineer to lead the server-side development of our Energy Management System applications. This system serves as the intelligence behind our battery solutions, supporting automation and optimization of operations across various electricity markets. You will help design and implement robust, high-performance backend infrastructure that ensures scalability, reliability, and responsiveness. ■Job Scope - Design, develop, and maintain highly performant, reliable, scalable, and secure backend systems and server side logic for WebApp/ Mobile App/ Services pertaining to Energy management , Battery management , Charger management & Asset management - Oversee projects from conception to deployment, ensuring smooth execution and delivery to create a great and on-brand user experience. - Maintain and optimize the backend infrastructure to meet evolving project needs. - Collaborate with front-end developers to integrate user-facing elements with server-side logic. - Write clean, standardized, maintainable, testable, and reusable backend code to solve business and technical problems. ■Internal common IT tools - Google Workspace （Gmail, G-cal, Gmeet等） - Slack - Notion - Dialpad - SmartHR - Money Foward - Bakuraku etc. ■About Engineering and Research Division Our Engineering and Research Division consists of mainly three teams that handle end-to-end development of Hardware and software systems for Energy storage & power transfer solutions and services. Currently, approximately 50 specialists are engaged in the mission of advancing energy storage technologies and solutions. The Division is organized into the following teams: - Series Development: Responsible for prototyping, testing & validation , requirements engineering , series handover of new products including product support and commissioning. - Advanced Engineering: Responsible for development and experimentation into emerging technologies to sustain our current and future roadmap of energy solutions with focus on a areas viz. embedded development, PCB design, model based development, battery management, power conversion, digital twins, edge computing, cloud solutions ,AI/ML based dispatch optimization, generation and forecasts. - Product Lifecycle Management: Manages product & project life cycles by tracking across quality gates through development, sourcing, value engineering leading up to manufacturing and after sales activities through cross functional coordination and data intensive product life cycle assessment Working alongside talented engineers from around the world, you will have the opportunity to thrive in a diverse environment that values autonomy and empowers individuals to make effective contributions while gaining new skills and experiences on some of the latest emerging technologies directly applied into our solutions.

■Engineering & Research Division / AI Data Center Architect ■About the role We are seeking an experienced AI Data Center Architect to join our team. In this role, you will be responsible for designing, implementing, and optimizing a medium-sized AI data center that leverages the latest hardware and software technologies to support our organization's growing AI and machine learning workloads. ■Job Scope 1. AI Data Center Architecture and Design: - Assess the organization's current and future AI and machine learning requirements, including compute, storage, and networking needs. - Design a scalable, resilient, and efficient AI data center architecture that can accommodate a variety of AI workloads, such as model training, inference, and data processing. - Ensure the architecture aligns with industry best practices, regulatory compliance, and the organization's IT and business strategies. 2. Hardware and Infrastructure Selection: - Evaluate and select the appropriate server hardware, including CPUs, GPUs, and specialized AI accelerators (e.g., NVIDIA Tensor Core GPUs, Google TPUs). - Determine the optimal storage solutions, considering factors like capacity, performance, and data redundancy (e.g., high-performance SSDs, NVMe, network-attached storage). - Ensure the networking infrastructure can support the required bandwidth, low-latency communication, and data transfer requirements. - Incorporate power and cooling considerations, such as efficient cooling systems and redundant power supplies, to maintain optimal operating conditions. 3. Software Stack Integration and Optimization: - Evaluate and integrate the appropriate operating system (e.g., Linux distributions), containerization platform (e.g., Docker, Kubernetes), and orchestration tools. - Integrate and configure leading AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Keras) to enable efficient model development and deployment. - Implement data management and processing pipelines, leveraging tools like Apache Spark, Hadoop, or custom data ingestion and preprocessing workflows. - Optimize the software stack for performance, scalability, and resource utilization to ensure the AI data center operates at peak efficiency. 4. Monitoring, Observability, and Automation: - Implement comprehensive monitoring and observability tools to track the performance, resource utilization, and health of the AI data center. - Develop data-driven insights and analytics to identify bottlenecks, optimize resource allocation, and ensure overall system reliability. - Automate deployment, scaling, and management processes to streamline the operation and maintenance of the AI data center. 5. Security and Compliance: - Implement robust security measures, such as access controls, network segmentation, and data encryption, to protect the AI data center from potential threats. - Ensure compliance with relevant data privacy and regulatory requirements (e.g., GDPR, HIPAA) by implementing appropriate data governance and access policies. - Develop and test disaster recovery and business continuity plans to ensure the resilience of the AI data center in the event of failures or disasters. 6. Continuous Optimization and Scalability: - Continuously monitor the AI data center's performance and resource utilization to identify opportunities for optimization. - Implement auto-scaling and dynamic resource allocation mechanisms to handle fluctuations in workload demands. - Explore options for distributed or federated learning architectures to scale the AI capabilities across multiple edge devices or smaller data centers. 7. Collaboration and Knowledge Sharing: - Provide technical leadership and mentor junior engineers on AI data center best practices and strategies. - Collaborate with cross-functional teams (data science, IT operations, security) to ensure the AI data center meets the organization's evolving needs. - Document and share knowledge, solutions, and lessons learned to promote continuous improvement within the organization. ■Internal common IT tools - Google Workspace （Gmail, G-cal, Gmeet等） - Slack - Notion - Dialpad - SmartHR - Money Foward - Bakuraku etc. ■About Engineering and Research Division Our Engineering and Research Division consists of mainly three teams that handle end-to-end development of Hardware and software systems for Energy storage & power transfer solutions and services. Currently, approximately 50 specialists are engaged in the mission of advancing energy storage technologies and solutions. The Division is organized into the following teams: - Series Development: Responsible for prototyping, testing & validation , requirements engineering , series handover of new products including product support and commissioning. - Advanced Engineering: Responsible for development and experimentation into emerging technologies to sustain our current and future roadmap of energy solutions with focus on a areas viz. embedded development, PCB design, model based development, battery management, power conversion, digital twins, edge computing, cloud solutions ,AI/ML based dispatch optimization, generation and forecasts. - Product Lifecycle Management: Manages product & project life cycles by tracking across quality gates through development, sourcing, value engineering leading up to manufacturing and after sales activities through cross functional coordination and data intensive product life cycle assessment Working alongside talented engineers from around the world, you will have the opportunity to thrive in a diverse environment that values autonomy and empowers individuals to make effective contributions while gaining new skills and experiences on some of the latest emerging technologies directly applied into our solutions.

■Engineering & Research Division / AI Data Center DC Block Architect ■About the role We are seeking an experienced AI Data Center DC Block Architect to join our team. In this role, you will be responsible for designing, developing, and optimizing modular, pre-engineered DC blocks that are tailored to support our organization's growing AI and machine learning workloads. ■Job Scope 1. AI Workload Analysis and Requirements: - Assess the organization's current and future AI and machine learning requirements, including compute, storage, and networking needs. - Collaborate with data science and IT teams to understand the specific performance, scalability, and reliability requirements of the AI workloads. - Identify any unique hardware or software considerations for the AI DC blocks, such as the need for specialized accelerators or optimized software stacks. 2. DC Block Architecture and Design: - Design modular, pre-engineered DC blocks that can efficiently support a variety of AI and machine learning workloads. - Ensure the DC block architecture is scalable, resilient, and aligned with industry best practices and the organization's overall data center strategy. - Optimize the DC block layout, power, cooling, and infrastructure to maximize performance, energy efficiency, and density. 3. Hardware Selection and Integration: - Evaluate and select the appropriate server hardware, including CPUs, GPUs, and specialized AI accelerators (e.g., NVIDIA Tensor Core GPUs, Google TPUs). - Determine the optimal storage solutions, considering factors like capacity, performance, and data redundancy (e.g., high-performance SSDs, NVMe, network-attached storage). - Integrate the networking infrastructure to support the required bandwidth, low-latency communication, and data transfer requirements of the AI workloads. 4. Software Stack Development and Optimization: - Design and develop the software stack for the AI DC blocks, including the operating system, containerization platform, and orchestration tools. - Integrate and configure leading AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Keras) to enable efficient model development and deployment. - Implement data management and processing pipelines, leveraging tools like Apache Spark, Hadoop, or custom data ingestion and preprocessing workflows. - Optimize the software stack for performance, scalability, and resource utilization to ensure the AI DC blocks operate at peak efficiency. 5. Monitoring and Observability: - Develop comprehensive monitoring and observability capabilities for the AI DC blocks, including metrics, logging, and tracing. - Implement data-driven insights and analytics to identify performance bottlenecks, optimize resource allocation, and ensure overall system reliability. - Automate deployment, scaling, and management processes to streamline the operation and maintenance of the AI DC blocks. 6. Security and Compliance: - Incorporate robust security measures, such as access controls, network segmentation, and data encryption, into the AI DC block design. - Ensure compliance with relevant data privacy and regulatory requirements (e.g., GDPR, HIPAA) by implementing appropriate data governance and access policies. - Develop and test disaster recovery and business continuity plans to ensure the resilience of the AI DC blocks in the event of failures or disasters. 7. Continuous Optimization and Scalability: - Continuously monitor the performance and resource utilization of the AI DC blocks to identify opportunities for optimization. - Implement auto-scaling and dynamic resource allocation mechanisms to handle fluctuations in AI workload demands. - Explore options for distributed or federated learning architectures to scale the AI capabilities across multiple edge devices or smaller data centers. ■Internal common IT tools - Google Workspace （Gmail, G-cal, Gmeet等） - Slack - Notion - Dialpad - SmartHR - Money Foward - Bakuraku etc. ■About Engineering and Research Division Our Engineering and Research Division consists of mainly three teams that handle end-to-end development of Hardware and software systems for Energy storage & power transfer solutions and services. Currently, approximately 50 specialists are engaged in the mission of advancing energy storage technologies and solutions. The Division is organized into the following teams: - Series Development: Responsible for prototyping, testing & validation , requirements engineering , series handover of new products including product support and commissioning. - Advanced Engineering: Responsible for development and experimentation into emerging technologies to sustain our current and future roadmap of energy solutions with focus on a areas viz. embedded development, PCB design, model based development, battery management, power conversion, digital twins, edge computing, cloud solutions ,AI/ML based dispatch optimization, generation and forecasts. - Product Lifecycle Management: Manages product & project life cycles by tracking across quality gates through development, sourcing, value engineering leading up to manufacturing and after sales activities through cross functional coordination and data intensive product life cycle assessment Working alongside talented engineers from around the world, you will have the opportunity to thrive in a diverse environment that values autonomy and empowers individuals to make effective contributions while gaining new skills and experiences on some of the latest emerging technologies directly applied into our solutions.

◾️Mission / ミッション As a core member of the ERD team, you will bridge the gap between ERD and commercial viability. You will define "should-cost" models from the earliest prototype phase to ensure PowerX’s products are not only technologically superior but also market-dominant in cost structure. ERDチームのコアメンバーとして、最先端のBESSの研究開発と事業性の架け橋となります。事前原価計算の手法を駆使し、プロトタイプの初期段階からあるべき原価（Should-cost）を定義して、PowerX製品が技術的に優れているだけでなく、コスト構造においても市場での優位性を確保する。 ◾️Key Responsibilities / 職務内容 - Should-Cost Modeling:Build high-precision, bottom-up cost models for prototypes. - Design-to-Cost Leadership:Collaborate with R&D engineers to optimize designs by providing real-time cost transparency and technical alternatives. - Strategic Procurement Integration: Support the procurement team in high-stakes supplier negotiations using data-driven "should-cost" arguments. - Value Engineering : Identify opportunities for cost reduction through material substitution, manufacturing process optimization (cycle-time analysis), and modularization. - Scalability Roadmap: Project cost trajectories from prototype to mass production, identifying Capex/Opex trade-offs. - Should-Costモデリング: プロトタイプのボトムアップ型高精度原価モデルの構築。 - Design-to-Costの主導:R&Dエンジニアと連携し、設計変更に対するコストの透明化と代替案提示を行い、設計段階での最適化を推進。 - 戦略的調達支援:根拠ある「あるべき原価」に基づき、サプライヤーとの高度な価格交渉を購買チームと共にリード。 - バリューエンジニアリング:素材置換、製造プロセス最適化（サイクルタイム分析）、モジュール化によるコストダウン機会の創出。 - 量産化ロードマップ策定:プロトタイプから量産に至るコスト推移を予測し、Capex/Opexのトレードオフを最適化。 ■社内共通ITツール - Google Workspace （Gmail, G-cal, Gmeet等） - Slack - Notion - Dialpad - SmartHR - Money Foward - バクラク 等

■Engineering & Research Division / IoT Security(ZTN) / Cloud & Edge Security Engineer ■About the role We’re seeking a Security Engineer to implement Zero Trust architecture for our IoT-based battery storage systems. You’ll secure device onboarding, manage cloud IoT integration, enforce access policies, and develop secure applications. ■Job Scope - Design, implement, and manage zero trust architectures for IoT and edge devices. - Secure device onboarding, certification, and communication channels. - Develop and enforce access policies aligned with zero trust principles. - Integrate IoT platforms (Azure IoT, AWS IoT) with security tools. - Implement device management and remote update mechanisms. - Develop secure web applications for device management. - Monitor security posture and respond to threats. - Collaborate with cross-functional teams for scalable deployment and security compliance. ■Internal common IT tools - Google Workspace （Gmail, G-cal, Gmeet等） - Slack - Notion - Dialpad - SmartHR - Money Foward - Bakuraku etc. ■About Engineering and Research Division Our Engineering and Research Division consists of mainly three teams that handle end-to-end development of Hardware and software systems for Energy storage & power transfer solutions and services. Currently, approximately 50 specialists are engaged in the mission of advancing energy storage technologies and solutions. The Division is organized into the following teams: - Series Development: Responsible for prototyping, testing & validation , requirements engineering , series handover of new products including product support and commissioning. - Advanced Engineering: Responsible for development and experimentation into emerging technologies to sustain our current and future roadmap of energy solutions with focus on a areas viz. embedded development, PCB design, model based development, battery management, power conversion, digital twins, edge computing, cloud solutions ,AI/ML based dispatch optimization, generation and forecasts. - Product Lifecycle Management: Manages product & project life cycles by tracking across quality gates through development, sourcing, value engineering leading up to manufacturing and after sales activities through cross functional coordination and data intensive product life cycle assessment Working alongside talented engineers from around the world, you will have the opportunity to thrive in a diverse environment that values autonomy and empowers individuals to make effective contributions while gaining new skills and experiences on some of the latest emerging technologies directly applied into our solutions.

【Engineering & Research Division / DevOps/SRE(Site Reliability Engineer)】 ■ Key responsibilities - Kubernetes・各種Cloud serviceを用いたサービス基盤の設計・構築・運用 - Bazelを用いた社内ビルドシステムおよびCI/CDの開発・運用 - プロダクト生産におけるネットワーク・ソリューション構築 - IoT ネットワーク・Deployシステムの設計・開発 ・Design, build, and operate service platforms using Kubernetes and various cloud services ・Develop and operate internal build systems and CI/CD pipelines using Bazel ・Build network solutions for product deployment and production environments ・Design and develop IoT networks and deployment systems ■ Why you’ll enjoy this role - 蓄電池を利用した新しいサービスにおける高い信頼性を実現するといったチャレンジ - 優秀なSWEと働くことのできる環境 - 自らが設計・技術選択を行い進めていくことができる ・Tackle the challenge of delivering highly reliable new services powered by battery energy storage systems ・Work in the team of talented software engineers ・Take ownership of architecture design and technology selection, with real influence over technical decisions ■ About the team - SRE/DevOpsチームでは、PowerXのサービスにおける重要な基盤を高いクオリティで実現し、より迅速に・スマートにビジネスを推進させるためのシステム開発・運用を行っています ・The SRE/DevOps team is responsible for building and operating high-quality core platforms that support PowerX’s services, enabling the business to move faster and smarter ■社内共通ITツール - Google Workspace （Gmail, G-cal, Gmeet等） -Slack -Notion -Dialpad -SmartHR -Money Foward -バクラク 等 ※英語を使う職場環境のため、ご応募の際に英文CVのご提出もお願いいたします ※英語力を伸ばしたい方も大歓迎です

■Engineering & Research Division / Inventory Management Staff ■業務内容 - 納品物の検収 - 在庫管理（入庫・保管・出庫） - 固定資産管理（棚卸、新規登録、移動手続き、除去手続き） - 保守部品の管理（補充、発送） - 産業廃棄物の処理 - 設備の保守・管理 - 重量物の設置や移動とそれに係る労働基準の対応 ■仕事概要 開発部で使用する保管部品や機器の管理業務を担当していただきます。タイトな開発日程の中で開発活動を滞りなく進めるために在庫管理は非常に重要です。開発日程を把握した上で必要となる部材の在庫を計画的に管理することが求められます。また、不具合対応や保守のために現場担当者と連携しながら装置のダウンタイムが最短となるように必要となる部材を現場へ発送をする段取りをしていただきます。 在庫管理以外にも製品の性質上、重量が非常に重いため安全に配慮しながら設置や搬出の段取りを計画し、重機や作業を外部の業者と打ち合わせをしていただきます。 ■社内共通ITツール - Google Workspace （Gmail, G-cal, Gmeet等） -Slack -Notion -Dialpad -SmartHR -Money Foward -バクラク 等