Senior Staff Cloud Architect

Samsung Semiconductor•San Jose, CA

22h•Onsite

About The Position

We are looking for a Sr. Staff Cloud Architect to lead the strategic design and evolution of our high-performance computing (HPC) and Semiconductor (EDA) cloud ecosystems. This role is designed for a technical visionary who can modernize traditional silicon engineering workflows by integrating scalable cloud-native architectures, advanced automation, and AI-driven insights. You will be responsible for building the bridge between legacy on-premises constraints and the limitless scale of the cloud, ensuring our engineering teams have the most efficient, automated, and performant environment in the industry.

Requirements

B.S. or higher in Computer Science, Electrical Engineering, or a related field, with relevant experience of 15+ years (B.S.), 13+ years (M.S.), or 10+ years (PhD), or equivalent.
5+ years dedicated to Semiconductor/EDA ecosystem support.
Proven expertise in AWS and Azure architecture, including container orchestration (EKS/AKS), serverless computing, and Infrastructure as Code (Terraform, CDK, or CloudFormation).
Expert-level knowledge of Linux kernel tuning and distributed file systems.
Deep understanding of the silicon design lifecycle, along with the hardware and software requirements of leading EDA vendors (e.g., Cadence, Synopsys, Siemens EDA).
Advanced scripting and programming skills in Python and Go, with a track record of building custom automation tooling.
Hands-on experience deploying ML models or LLMs to optimize IT operations (AIOps) or forecast resource consumption.

Nice To Haves

Professional proficiency in both English and Korean is strongly preferred, enabling effective leadership of complex technical discussions and documentation across diverse stakeholders.

Responsibilities

Architect and manage multi-region, large-scale cloud environments (AWS/Azure) specifically optimized for compute-intensive EDA workloads (Simulation, Characterization).
Design and optimize distributed scheduling systems (e.g., LSF, Slurm, or Grid Engine) to manage tens of thousands of concurrent jobs with maximum throughput and cost efficiency.
Pioneer the use of Generative AI and Machine Learning to predict workload peaks, automate infrastructure scaling, and develop agentic frameworks for autonomous system health management.
Implement high-performance data strategies using enterprise storage solutions (e.g., FSx, NetApp) and hybrid-cloud caching to minimize latency in global collaborative environments.
Establish robust, secure-by-design frameworks for sensitive engineering data, ensuring compliance with global semiconductor IP protection standards.
Act as a strategic bridge between the US-based infrastructure team and the headquarters in Korea to ensure architectural alignment and seamless workload migration.
Act as a thought leader by publishing technical guidance, participating in industry standard-setting bodies, and mentoring a global team of infrastructure engineers.