Research Scientist - Loop Engineering and AI Infrastructure

SK hynix memory solutions America Inc.•San Jose, CA

3d•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

Hands-on experience with an AI-assisted hardware/software co-design loop framework, such as CHIA, 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.

Responsibilities

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.
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.