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Overview
- NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years.
- It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.
- NVIDIA is looking for a Manager of Solution Architecture to lead NVIDIA Infrastructure Specialist Team, Continuous bringup and optimization.
- Academic and commercial groups around the world are using NVIDIA products to redefine deep learning and data analytics, and to power data centers.
- We are building many of the largest and fastest AI/HPC systems in the world! We are looking for someone with the ability to work on a dynamic customer focused team that requires excellent interpersonal skills.
- This role will be interacting with customers, partners and internal teams, to analyze, define and implement large scale projects.
- The scope of these efforts includes a combination of Networking, System Design and Automation and being the face to the customer! What you'll be doing: Lead a team dedicated to consulting, optimizing, and improving the resiliency of customer AI factory infrastructures, ensuring high service quality and operational perfection.
- Drive hands-on infrastructure analysis and tuning of complex GPU-accelerated systems, AI workloads, and datacenter environments, identifying areas for efficiency gains and operational improvements.
- Work closely with internal teams (Engineering, Product, Sales) and customer collaborators to align infrastructure strategies with business goals, enabling smooth, scalable AI deployments.
- Act as a technical authority on NVIDIA GPU, CPU and networking technologies, supporting customer discussions, architecture reviews.
- Establishing and evolving optimization and monitoring methodologies, using analytics and tooling to detect bottlenecks, reduce downtime, and ensure system health at scale.
- Participate in customer-facing engagements, including roadmap sessions, post-deployment reviews, and incident retrospectives, helping to craft the customer experience and influence NVIDIA’s infrastructure strategy.
- What we need to see: Over 4 years leading teams and 8+ overall years in service operations in large data centers, focusing on infrastructure performance.
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field, with shown technical leadership in data center, server, and network operations.
- Proficiency in both Japanese and English, demonstrating clear communication of technical topics across multicultural teams and with customers.
- Deep expertise in data center architecture and operations, including servers, GPUs, NICs, networking topologies, storage systems, and Linux-based environments.
- Strong analytical, solving problems, and decision-making skills, capable of identifying root causes, driving continuous improvement, and delivering resilient technical solutions.
- Strong communication, time management, and organizational skills, along with experience in leading complex projects, guiding technical teams, and meeting important metrics.
- Ways to stand out from the crowd: Deep familiarity with AI infrastructure and workflows, including training/inference pipelines, MLOps/DevOps tools, containerization (Docker, Kubernetes), and large-scale system deployments.
- Knowledge of data center infrastructure operations, including safety, security, environmental controls, and standard operating procedures.
- Strong interpersonal and collaboration skills, with the ability to lead discussions, influence outcomes, and build positive relationships with both internal and external collaborators.
- NVIDIA is widely considered to be one of the technology world’s most desirable employers.
- We have some of the most forward-thinking and hardworking individuals in the world working for us.
- If you're creative and autonomous, we want to hear from you!.
Sourced directly from NVIDIA’s career page
Your application goes straight to NVIDIA.
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Specialisation
Open roles at NVIDIA
1997 positions
Job ID
/job/Japan-Tokyo/Manager--Solutions-Architecture---Continuous-Bringup-and-Optimization_JR2016732-1
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