SK hynix America · 1 month ago
AI/ML Cluster System Design Engineer, Contractor
San Jose, CA
Contract
Onsite
Senior Level, Lead/Staff
$150K/yr - $200K/yr
8+ years expSK hynix America is a leader in semiconductor innovation, developing advanced memory solutions. They are seeking an AI/ML Cluster System Design Engineer to design and optimize large-scale GPU clusters for AI/ML workloads, ensuring performance and operational efficiency.
Semiconductors
Responsibilities
Architect robust, scalable, and efficient computing clusters that maximize AI workload performance while meeting operational and budgetary constraints
Collaborate across hardware capabilities and AI/ML framework requirements, translating model training needs and inference performance targets into concrete system specifications
Design end-to-end cluster architectures that encompass compute resources, networking fabric, storage subsystems, and power/cooling integration
Select appropriate GPU platforms based on workload characteristics, designing network topologies that minimize communication bottlenecks in distributed training scenarios
Architect storage solutions that can sustain the high-throughput demands of large-scale AI operations
Conduct detailed performance modeling and capacity planning exercises, predicting cluster behavior under various workload scenarios and identifying potential bottlenecks before deployment
Guide decisions on cluster topology, including considerations for rail-optimized designs, spine-leaf architectures, and direct GPU-to-GPU connectivity technologies such as NVLink and InfiniBand configurations
Understand and plan for the infrastructure requirements that support cluster operations, which includes calculating aggregate power requirements based on GPU selection and cluster scale, specifying cooling capacity needed to maintain optimal operating temperatures, determining network bandwidth requirements for different training paradigms, and identifying facility-level dependencies that impact cluster deployment feasibility
Contribute your expertise by conducting architecture reviews, optimize existing cluster configurations, and prototype new design approaches
Provide technical guidance on emerging technologies in AI accelerators, networking, and infrastructure, evaluate vendor solutions against architectural requirements, and benchmark alternative designs
Contribute insights that shape both immediate deployment plans and long-term infrastructure strategy and ensure AI computing capabilities remain competitive, efficient, and future-ready
Qualification
Required
Proven experience designing and deploying large-scale AI/ML clusters in production environments, including clusters with 100+ GPUs
Direct involvement in hardware selection, network design, and performance optimization for AI workloads
Hands-on expertise with modern GPU architectures from NVIDIA or AMD, plus familiarity with emerging AI accelerator technologies
Comprehensive knowledge of AI/ML frameworks and their infrastructure requirements, including PyTorch and distributed training libraries such as DeepSpeed, Megatron-LM, and Ray
Understanding of how framework-specific optimizations impact cluster design decisions and how architectural choices affect model training efficiency and scalability
Strong background in high-performance networking, including designing low-latency, high-bandwidth network fabrics (e.g., InfiniBand, RoCE, or proprietary interconnects)
Understanding of network topology implications for distributed training patterns, including all-reduce operations, parameter server architectures, and pipeline parallelism
Practical experience integrating cluster design decisions with facility requirements, including Power density considerations based on GPU selection, cooling architecture for varying cluster sizes, and space optimization and data center infrastructure alignment
Ability to collaborate effectively with facility engineers to ensure clusters are operationally feasible
Preferred
Bachelor's degree in engineering and science discipline with training that matches standard college level training for computer engineering
8+ years of professional experience in systems architecture
Minimum 3 years dedicated to AI/ML infrastructure design and deployment
Track record of designing clusters supporting diverse workloads from large language model training, to high performance computing and/or computer vision applications
Deep understanding of how workload characteristics influence architectural decisions
Proven ability to balance technical performance with practical constraints such as budget, timeline, and operational feasibility
Company
SK hynix America
Semiconductors are essential to all IT products, and its performance often determines the performance of the final products.
201-500 employees
H1B Sponsorship
SK hynix America has a track record of offering H1B sponsorships. Please note that this does not
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2025 (2)
2024 (16)
2023 (3)
2022 (3)
2021 (2)
2020 (2)
Funding
Current Stage
Growth StageLeadership Team
Jennifer Lee
Director of Technology / Evangelist : Pathfinding & Partnerships
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