ReRAM Memory and AI Development Engineer

onsemi•Gresham, OR

10h

About The Position

The ReRAM Memory and AI Development Engineer advances resistive random-access memory (ReRAM) technology and develops AI-enabled solutions to improve semiconductor engineering productivity and technical capability. The role combines hands-on ReRAM technology development with applying machine learning, generative AI, physics-informed AI, and AI agents across semiconductor engineering. The engineer bridges semiconductor device knowledge, data science, AI/ML, EDA, and software development to turn concepts into practical engineering solutions. The position includes developing and characterizing ReRAM technology; creating device, statistical, and behavioral models; analyzing device and array data; and collaborating across process integration, device, modeling, design, reliability, and test teams. It also involves evaluating ReRAM for embedded memory, compute-in-memory, and other emerging applications. Across semiconductor development more broadly, the engineer identifies opportunities for AI to reduce manual effort, accelerate analysis, improve decision-making, or enable new capabilities. The role prototypes emerging AI technologies, transitions successful concepts into practical engineering tools, and promotes adoption through collaboration, demonstrations, documentation, and knowledge sharing.

Requirements

Ph.D. or advanced degree in Electrical Engineering, Computer Engineering, Applied Physics, Computer Science, or a related field.
Strong foundation in semiconductor devices, memory technologies, EDA, or semiconductor engineering.
Experience with machine learning, deep learning, and scientific computing.
Strong 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.
Strong cross-functional communication and technical problem-solving skills.

Nice To Haves

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.

Responsibilities

Develop and characterize ReRAM technology, including 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.
Collaborate with process integration, device, modeling, design, reliability, and test teams to advance ReRAM technology.
Evaluate ReRAM for embedded memory, compute-in-memory, and other emerging applications.
Develop and deploy AI solutions to improve engineering productivity, technical capability, and decision-making across semiconductor development.
Apply machine learning, generative AI, physics-informed AI, and AI agents to engineering problems such as device and process modeling, engineering data analysis and visualization, simulation and design-space exploration, EDA and design automation, model generation and optimization, technical knowledge retrieval and synthesis, and workflow, documentation, and repetitive-task automation.
Identify high-value opportunities where AI can reduce manual effort, accelerate analysis, or enable new engineering capabilities.
Prototype emerging AI technologies and transition successful concepts into practical tools for engineering teams.
Promote effective AI adoption through technical collaboration, demonstrations, documentation, and knowledge sharing.