Research Scientist Jobs

About The Position

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.

Benefits

• medical
• dental
• vision
• life insurance
• company 401(k) match
• cafeteria
• onsite gym