Job description

About the Company: At SK hynix memory solutions, 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 NAND flash technology and memory solutions, 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 memory solutions, you will 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. About the Role: We are hiring a full-time research scientist. Depending on your qualifications, the job is loop engineering, AI infrastructure research, or both. Loop engineering is the design and study of closed-loop flows that automate, optimize, and explore SoC designs. These are AI-assisted hardware/software co-design flows. CHIA, from UC Berkeley, is one example of a framework you may use. AI infrastructure research is full-stack design and optimization of AI systems from storage devices to the host: how memory-device noise affects AI performance, where the system bottlenecks, and which compute-memory architectures are worth building. You will own the research questions, methods, and written results. What you will do: Establish best practices for SoC design automation, optimization, and design-space exploration, and make them the way the team runs design studies. Design and implement co-design loops whose steps run SoC tools, simulators, software builds, physical-design feedback, and AI agents, and whose outputs feed the next iteration. Build those flows with the languages and tools the study needs, including Chisel and XLS, simulators, software builds, and ASIC or FPGA CAD. Measure whether the designs a loop produces are correct, and compare them on power, speed, silicon area, or how well the target workload runs. Record which design was chosen, what the search cost, and enough detail that someone else can rerun the study and see why a run failed. Research: AI infrastructure: Study AI system design from storage and memory devices through controllers, interconnects, and the host software path used by training and inference. Measure how device noise and non-idealities (read and write noise, variation, retention, disturb, and error rates) affect model quality, tail latency, throughput, and energy. Quantify bottlenecks in capacity, bandwidth, latency, power, and data movement, and state what they imply for architecture. Evaluate compute-memory architectures such as near-memory and in-memory compute, computational storage, and disaggregated memory. What you bring: Bachelor’s, master’s, or Ph.D. in electrical engineering, computer engineering, or computer science. A bachelor’s degree should include research depth in computer architecture, computer systems, VLSI, or electronic design automation. A record of taking a research question to a measured, written result. Strong in Python. For a loop-engineering assignment: Experience automating an SoC, RTL, or physical-design flow, including design-space exploration or closed-loop optimization. Ability to design interfaces between design tools so a flow can be reused. For an AI infrastructure assignment: Research on the path from storage or memory devices to a host running an AI workload, and on how device behavior, system bottlenecks, or compute-memory architecture affect AI performance. Preferred Qualifications: Hands-on experience with an AI-assisted hardware/software co-design loop framework, such as CHIA, SKHY, or with another agentic or scripted flow. Proficiency with Chisel, XLS, or RISC-V SoC integration, and with RTL simulation or FPGA prototyping. Experience with commercial ASIC flows, and with reading timing, power, and area reports. Experience with architecture simulators. Background in memory or storage reliability: device noise, ECC, endurance, or error models. Papers or patents in computer architecture, VLSI CAD, or hardware/software co-design. What success looks like: In the first year, a loop-engineering hire produces a reusable co-design loop for an SoC decision and a written best practice. An AI infrastructure hire produces a result that ties a bottleneck, a memory-device noise effect, or a compute-memory architecture to host-level AI performance. A hire for both produces both. COMPENSATION: $000,000/yr - $000,000/yr REGARDING COMPENSATION: SK hynix memory solutions America Inc. offers you the opportunity to apply your skills to exciting projects while working with innovative teams. Our compensation package is complimented by a generous benefits package including medical, dental, vision, life insurance and a company 401(k) match, as well as cafeteria, onsite gym and much more. If you are motivated by technical challenges, we offer a collaborative work environment that encourages career growth. The salary offered to a selected candidate will be tailored based on several factors, including the location, job grade, relevant knowledge, skills, and experience. We also take into account the internal equity among our current team members to ensure fairness and competitiveness.

Requirements

Must-haves

We are hiring a full-time research scientist. Depending on your qualifications, the job is loop engineering, AI infrastructure research, or both.
You will own the research questions, methods, and written results.
Establish best practices for SoC design automation, optimization, and design-space exploration, and make them the way the team runs design studies.
Design and implement co-design loops whose steps run SoC tools, simulators, software builds, physical-design feedback, and AI agents, and whose outputs feed the next iteration.
Build those flows with the languages and tools the study needs, including Chisel and XLS, simulators, software builds, and ASIC or FPGA CAD.
Study AI system design from storage and memory devices through controllers, interconnects, and the host software path used by training and inference.
Measure how device noise and non-idealities (read and write noise, variation, retention, disturb, and error rates) affect model quality, tail latency, throughput, and energy.
Quantify bottlenecks in capacity, bandwidth, latency, power, and data movement, and state what they imply for architecture.
Evaluate compute-memory architectures such as near-memory and in-memory compute, computational storage, and disaggregated memory.
A record of taking a research question to a measured, written result.
Strong in Python.
Experience automating an SoC, RTL, or physical-design flow, including design-space exploration or closed-loop optimization.
Ability to design interfaces between design tools so a flow can be reused.
Research on the path from storage or memory devices to a host running an AI workload, and on how device behavior, system bottlenecks, or compute-memory architecture affect AI performance.
COMPENSATION: $000,000/yr - $000,000/yr

Nice-to-haves

Hands-on experience with an AI-assisted hardware/software co-design loop framework, such as CHIA, SKHY, or with another agentic or scripted flow.
Proficiency with Chisel, XLS, or RISC-V SoC integration, and with RTL simulation or FPGA prototyping.
Experience with commercial ASIC flows, and with reading timing, power, and area reports.
Experience with architecture simulators.
Background in memory or storage reliability: device noise, ECC, endurance, or error models.
Papers or patents in computer architecture, VLSI CAD, or hardware/software co-design.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaipython

Platform: greenhouse
Effort: quick

Benefits

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Dental

Vision

401(K)