Develop resistive random-access memory (ReRAM) technology and apply PyTorch and physics-informed AI across semiconductor engineering.
About the role
This role combines resistive random-access memory (ReRAM) technology development with the application of machine learning, generative AI, physics-informed AI, and AI agents across semiconductor engineering. You will bridge semiconductor device physics, data science, EDA, and software development to build device models and practical AI-enabled engineering workflows.
What you'll do
- Develop and characterize ReRAM technology, evaluating SET/RESET behavior, resistance distributions, variability, endurance, reliability, and operating margins.
- Develop device, statistical, and behavioral models that link ReRAM characteristics to circuit and system requirements.
- Analyze device and array data to support technology optimization, design enablement, and reliability assessment.
- Evaluate ReRAM for embedded memory, compute-in-memory, and emerging applications.
- Develop and deploy machine learning, generative AI, physics-informed AI, and AI agents for device and process modeling, simulation, EDA automation, and data analysis.
- Prototype AI tools and transition functional concepts into workflows for engineering teams across semiconductor development.
What you'll need
- Ph.D. or advanced degree in Electrical Engineering, Computer Engineering, Applied Physics, Computer Science, or a related field.
- Foundation in semiconductor devices, memory technologies, EDA, or semiconductor engineering.
- Experience with machine learning, deep learning, and scientific computing.
- Python programming skills and experience with frameworks such as PyTorch or equivalent.
- Experience developing data-driven or physics-informed engineering models.
- Ability to translate semiconductor engineering challenges into modeling, AI, software, or automation solutions.
Nice to have
- Experience with ReRAM or other emerging memory technologies, including device characterization, modeling, variability, endurance, or reliability.
- Experience with physics-informed machine learning, generative AI, probabilistic modeling, LLMs, or AI agents.
- Experience with semiconductor EDA and simulation tools such as Cadence Virtuoso, Synopsys Custom Compiler, Sentaurus TCAD, HSPICE, or Verilog-A.
- Experience with compute-in-memory, memory-aware AI, or neural-network quantization.
- Experience developing reusable engineering automation, analysis, or AI software tools.
Skills: ReRAM, SET/RESET, device characterization, TCAD, PyTorch, compute-in-memory
More semiconductor jobs: onsemi jobs · Oregon jobs
This role has been open 10 days — well below the 72-day median for Memory Design roles.
Memory Design · Memory Design
|
Open roles in category
302
|
Median days open
72 d
|
Category median salary (all employers)
$205k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | onsemi | All employers we track in this specialty (302 roles · 29 employers) |
|---|---|---|
| Open roles in this specialty | 7 | 302 |
| Median days open | 108 d | 72 d (+36 d vs this employer) |
| Median salary (USD postings) | — | $205k |
Skills observed across this category: ReRAM, SET/RESET, device characterization, TCAD, PyTorch, compute-in-memory
Who's hiring in this category
- Micron Technology · 154 open roles · median 82 d
- Synopsys · 30 open roles · median 77 d
- Qualcomm · 18 open roles · median 106 d
- Broadcom · 16 open roles · median 78 d
- SanDisk · 15 open roles · median 7 d
- NVIDIA · 8 open roles · median 59 d
How we counted: 302 open Memory Design (Memory Design) roles from 29 employers tracked in the SemiconductorJobs index, counted 6 Nov 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there. Figures refresh nightly.