Analog Devices
Semiconductor
StaffEngineer,Post-SiliconValidation(AI-EnabledWorkflows)
Neural analysis suggests this role is
optimal for Staff candidates.
“Staff Engineer, Post-Silicon Validation (AI-Enabled Workflows) at Analog Devices. Skills: Post-silicon validation, AI/ML, Agentic AI systems. Identify AI/ML improvement opportunities. Apply existing AI/ML technologies”
What You'll Achieve.
Accelerate post-silicon workflows; Scale post-silicon workflows; Improve post-silicon workflows; Reduce measurement effort; Improve insight; Shorten time-to-confidence
Industry & Context.
Debug silicon issues; Root cause analysis
10% travel
What They're Looking For.
Must Have
8+ years post-silicon experience, Mixed-signal/high-speed digital blocks experience, Silicon bring-up knowledge, Measurement instrumentation knowledge, ATE test/characterization knowledge, Debug silicon issues with limited observability, Apply ML techniques to measurement data, Experience using AI-enabled tools, Comfortable in Python-based environments, Reason about data quality, bias, limits, Cross-functional communication, Technical leadership experience, Operate across abstraction levels
Nice to Have
Adaptive/data-driven characterization experience, Yield learning experience, Post-silicon tuning experience, Reinforcement learning exposure, Bayesian optimization exposure, Active learning exposure, RF test/characterization familiarity, Lab automation platforms experience, Test data analytics platforms experience, AI-assisted debug/analysis tools experience, Industry conference participation, Standards group participation, Technical publication experience
What You'll Do.
Identify AI/ML improvement opportunities
Apply existing AI/ML technologies
Integrate existing AI/ML technologies
Serve as technical bridge
Evaluate AI-enabled tools
Prototype AI-enabled tools
Develop reference workflows
Create reusable frameworks
Represent organization in reviews
Represent organization in vendor engagements
Represent organization in industry forums
How You'll Work.
Team & Collaboration
Cross-functional influence; Bridge silicon/test engineers; Bridge ML specialists; Bridge external vendors; Bridge academic partners; Bridge ecosystem partners
Communication Scope
Cross-functional communication; Technical reviews; Vendor engagements; Industry forums
Full Job Description
**About Analog Devices** Analog Devices, Inc. (NASDAQ: _[ADI](http://finance.yahoo.com/q?s=ADI)_ ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's Possible™. Learn more at _[www.analog.com](https://www.analog.com/)_ and on _[LinkedIn](https://www.linkedin.com/company/analog-devices/posts/?feedView=all)_ and _[Twitter (X)](https://x.com/adi_news)_. # Staff Engineer, Post Silicon Validation ## About the Role We are seeking a **Staff Engineer** to lead the application of **AI-enabled techniques to post-silicon test, evaluation, and characterization** across mixed-signal and SoC products. This role sits within Engineering Enablement and is intended for a **deeply experienced post-silicon engineer** who understands how silicon is actually evaluated in labs and on ATE—and who can **strategically apply AI/ML and agentic tools** to accelerate, scale, and improve those workflows. This is a **hands-on, high-impact individual contributor role** with broad cross-functional influence. You will define _how_ AI is practically used in evaluation, characterization, calibration, trimming, and qualification—not as an abstract data science exercise, but as a tool to reduce measurement effort, improve insight, and shorten time-to-confidence in silicon. ## Core Responsibilities · Identify high-leverage opportunities where **AI/ML can materially improve post-silicon workflows** , such as: - Adaptive characterization (intelligent selection of next measurements) - Anomaly detection in parametric, waveform, and RF data - Measurement clustering and outlier id
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