AI/HPC System Engineer

About the role

This position is responsible for maintaining and advancing SK hynix memory technology leadership through the implementation of next-generation memory-centric architecture. The hired individual will serve as a critical bridge between hardware and software teams to deliver highly energy-efficient AI computing platforms. Success in this role demands deep system and software expertise within a fast-paced semiconductor innovation environment. The engineer will own the integration and optimization of system-level components to ensure robust performance and scalability. This role requires a proactive approach to identifying bottlenecks and driving technical solutions from conception to deployment. The position emphasizes close collaboration with cross-functional partners to align system capabilities with product objectives. Ultimately, the hire will be accountable for the reliability and efficiency of the core infrastructure that powers advanced memory solutions.

Key facts

What you'll do

Document technical specifications, design decisions, and test plans to ensure clarity, reproducibility, and long-term maintainability of system components.

Partner with industry ecosystem participants to refine system software design and architecture during technical discussions and collaborative sessions.

Maintain current knowledge of AI, memory systems, and system software trends to guide future platform roadmaps and strategic initiatives.

Communicate findings and proposals effectively to internal stakeholders and external collaborators to ensure alignment and shared understanding.

Analyze complex system behaviors and derive optimized solutions that balance performance, power, and area considerations.

Implement low-level system software and device drivers to support advanced memory technologies and heterogeneous compute architectures.

Conduct performance analysis and profiling to identify latency and throughput issues within the memory hierarchy and interconnects.

Collaborate with hardware designers to validate silicon features and bring-up firmware in early engineering stages.

Utilize scripting and programming skills to automate testing and validation workflows for rapid iteration and verification.

Contribute to the creation of reference designs and integration guidelines for internal and external developer communities.

Requirements

Ph.D. in Computer Science/Electrical Engineering or 6+ years of experience in system software development, with a focus on memory system architecture and AI infrastructure.

Strong understanding of computer architecture, memory systems, and system software design principles.

Proficiency in Python, C, and C++ programming languages for developing high-performance system tools.

Knowledge of operating systems internals, device drivers, and system programming techniques is essential.

Familiarity with GPU architecture and CUDA programming to optimize memory access patterns and kernel execution.

Excellent problem-solving skills for debugging complex system software issues in distributed and heterogeneous environments.

Strong communication and collaboration skills for working effectively with cross-functional teams and stakeholders.

Ability to manage multiple priorities and deliver high-quality results in a fast-paced, deadline-driven setting.

Nice to have

Experience in semiconductor or high-performance computing environments working on memory technologies and innovation.

Background in sustainable and energy-efficient computing platforms and their trade-offs.

Exposure to system-level validation frameworks and continuous integration pipelines.

Practical notes

Position requires onsite work at the San Jose location.

Equal Employment Opportunity applies to all qualified applicants.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.