Research Scientist: Loop Engineering & AI Infrastructure

SKHMS Corporation

San Jose, Northern (CA, KY)

Hybrid

USD 150,000 - 220,000

Full time

4 days ago
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Job summary

SK hynix memory solutions America Inc. is seeking a Research Scientist to lead loop engineering and AI infrastructure research in San Jose. You will own research questions, methods, and written results across loop engineering and AI systems, from storage devices to host software.

You will design co-design loops, measure performance, and explore memory-system bottlenecks, aiming to improve AI workloads and silicon efficiency. Strong in Python and academic research depth is preferred.

Qualifications

  • Bachelor’s, master’s, or Ph.D. in electrical engineering, computer engineering, or computer science.
  • A record of taking a research question to a measured, written result.
  • Strong in Python.

Responsibilities

  • Establish best practices for SoC design automation, optimization, and design-space exploration.
  • Design and implement co-design loops whose steps run SoC tools, simulators, software builds, physical-design feedback, and AI agents; outputs feed the next iteration.
  • Build flows with languages/tools such as Chisel and XLS, simulators, software builds, and ASIC/FPGA CAD.
  • Measure designs for correctness and compare on power, speed, silicon area, or workload performance; document results.

Skills

Python

Education

Electrical/Computer Engineering or Computer Science degree

Tools

Chisel
XLS
RISC‑V SoC integration
RTL simulation
FPGA prototyping

Job description

SK hynix memory solutions America Inc. is seeking a Research Scientist to lead loop engineering and AI infrastructure research in San Jose. You will own research questions, methods, and written results across loop engineering and AI systems, from storage devices to host software.

You will design co-design loops, measure performance, and explore memory-system bottlenecks, aiming to improve AI workloads and silicon efficiency. Strong in Python and academic research depth is preferred.

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