{"id":3286414,"url":"https://alion.io/job/onsemi-reram-memory-and-ai-development-engineer","title":"ReRAM Memory and AI Development Engineer","company":{"id":58717,"name":"onsemi","domain":"onsemi.com","url":"https://alion.io/company/onsemi","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":77,"open_postings":192,"ghost_share":0,"stale_share":0.901,"repost_share":0,"time_to_fill_p50_days":28,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gresham, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":133000,"max_usd":297000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Cadence Virtuoso","optional":true},{"name":"Quantization","optional":true},{"name":"Synopsys Custom Compiler","optional":true},{"name":"Synopsys HSPICE","optional":true},{"name":"TCAD","optional":true},{"name":"Verilog","optional":true}],"status":"live","first_seen_at":"2026-10-26T15:02:36Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-30T01:28:26Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"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.\nThe 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.\n ReRAM Technology Development\nDevelop and characterize ReRAM technology, including SET/RESET behavior, resistance distributions, variability, endurance, reliability, and operating margins.\nDevelop device, statistical, and behavioral models linking ReRAM characteristics to circuit and system requirements.\nAnalyze device and array data to support technology optimization, design enablement, and reliability assessment.\nCollaborate 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.\n\nAI-Enabled Engineering\nDevelop and deploy AI solutions to improve engineering productivity, technical capability, and decision-making across semiconductor development, not limited to ReRAM\nApply machine learning, generative AI, physics-informed AI, and AI agents to engineering problems such as:Device and process modeling\nEngineering data analysis and visualization\nSimulation and design-space exploration\nEDA and design automation\nModel generation and optimization\nTechnical knowledge retrieval and synthesis\nEngineering workflow, documentation, and repetitive-task automation\n\nIdentify high-value opportunities where AI can reduce manual effort, accelerate analysis, or enable new engineering capabilities.\nPrototype emerging AI technologies and transition successful concepts into practical tools for engineering teams.\nPromote effective AI adoption through technical collaboration, demonstrations, documentation, and knowledge sharing.\n 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.\nExperience with machine learning, deep learning, and scientific computing.\nStrong Python programming skills and experience with frameworks such as PyTorch or equivalent.\nExperience developing data-driven or physics-informed engineering models.\nAbility to translate semiconductor engineering challenges into modeling, AI, software, or automation solutions.\nStrong cross-functional communication and technical problem-solving skills.\nPreferred Qualifications\nExperience with ReRAM or other emerging memory technologies, including device characterization, modeling, variability, endurance, or reliability.\nExperience with physics-informed machine learning, generative AI, probabilistic modeling, LLMs, or AI agents.\nExperience with semiconductor EDA and simulation tools such as Cadence Virtuoso, Synopsys Custom Compiler, Sentaurus TCAD, HSPICE, or Verilog-A.\nExperience with compute-in-memory, memory-aware AI, or neural-network quantization.\nExperience developing reusable 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