AI/ML Computing Cluster Engineer

About SK hynix America
At SK hynix America, we're at the forefront of semiconductor innovation, developing advanced memory solutions that power everything from smartphones to data centers. As a global leader in DRAM and NAND flash technologies, we drive the evolution of advancing mobile technology, empowering cloud computing, and pioneering future technologies. Our cutting-edge memory technologies are essential in today's most advanced electronic devices and IT infrastructure, enabling enhanced performance and user experiences across the digital landscape.
We're looking for innovative minds to join our mission of shaping the future of technology. At SK hynix America, you'll be part of a team that's pioneering breakthrough memory solutions while maintaining a strong commitment to sustainability. We're not just adapting to technological change – we're driving it, with significant investments in artificial intelligence, machine learning, and eco-friendly solutions and operational practices. As we continue to expand our market presence and push the boundaries of what's possible in semiconductor technology, we invite you to be part of our journey to creating the next generation of memory solutions that will define the future of computing.

Job Overview:

As the AI/ML Computing Cluster engineer, you will work on development and operation of high-performance computing clusters supporting AI/ML workloads. You will be responsible for development, implementation, operation, and optimization of AI data center IT environments to ensure scalability, performance, reliability, and cost-effectiveness. This role requires collaboration with cross-functional teams to align computing infrastructure with the organization's strategic direction.

Responsibilities:

Computing Cluster Infrastructure Development

• Design and implement distributed computing cluster infrastructure to support large-scale AI/ML model training and inference jobs with a focus on transformer-based AI models.

• Build and maintain distributed system to ensure scalability, efficient resource allocation, and high throughput.
• Optimize cluster performance through hardware selection, equipment configuration, network engineering, and performance analysis.
• Deploy and operate data center networking infrastructure using software system for automation, design validation, deployment, and operational support.
• Implement tools and processes to maintain high uptime and ensure infrastructure reliability during both model training and inference phases.
• Identify and resolve performance bottlenecks, improving overall system throughput and response times.

Team Leadership & Collaboration

• Collaborate with cross-functional teams, including research, security, and benchmark test engineering teams, to integrate infrastructure with AI workflows, ensuring seamless deployment and operation.

• Engage with technology vendors and partners to evaluate new solutions to drive innovation in AI computing infrastructure.

Qualification:   

• Master’s degree or above in Computer Science, Electrical Engineering, or related fields.
• 2 years of experience in AI cluster engineering, MLOps, and benchmark testing, including GPU performance analysis, memory usage, and energy/power monitoring tools.
• Strong familiarity with AI computing architecture, AI/ML infrastructure requirements, memory architecture and usages in AI/ML, AI algorithm trends and best practices.

• Expertise in optimizing resource utilization, improving system throughput, and reducing latency in both training and inference.

Equal Employment Opportunity:

SKHYA is an Equal Employment Opportunity Employer. We provide equal employment opportunities to all qualified applicants and employees and prohibit discrimination and harassment of any type without regard to race, sex, pregnancy, sexual orientation, religion, age, gender identity, national origin, color, protected veteran or disability status, genetic information or any other status protected under federal, state, or local applicable laws. 

Compensation: