ReRAM Memory and AI Development Engineer

We are seeking a ReRAM Memory and AI Development Engineer to advance resistive random-access memory (ReRAM) technology and develop AI-enabled solutions that improve semiconductor engineering productivity and technical capability.

The role combines hands-on ReRAM technology development with the broader application of machine learning, generative AI, physics-informed AI, and AI agents across semiconductor engineering. The successful candidate will bridge semiconductor device knowledge, data science, AI/ML, EDA, and software development to turn new concepts into practical engineering solutions.

• ReRAM Technology Development
• Develop and characterize ReRAM technology, including SET/RESET behavior, resistance distributions, variability, endurance, reliability, and operating margins.
• Develop device, statistical, and behavioral models linking 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.
• AI-Enabled Engineering
• Develop and deploy AI solutions to improve engineering productivity, technical capability, and decision-making across semiconductor development, not limited to ReRAM
• 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
  Engineering 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.
  

• 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.
  Preferred Qualifications
  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.