Analog Devices
Semiconductor
StaffEngineer,Post-SiliconValidation(AI-EnabledWorkflows)
Neural analysis suggests this role is
optimal for Lead candidates.
“Staff Engineer, Post-Silicon Validation (AI-Enabled Workflows) at Analog Devices. Skills: AI-enabled techniques, Post-silicon validation, AI/ML, Agentic tools. Lead application of AI-enabled techniques to post-silicon test. Lead application of AI-enabled techniques to evaluation”
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 in silicon
Industry & Context.
Debug silicon issues with limited observability and noisy data; Reason about data quality, bias, observability limits, and measurement noise
Travel: Yes, 10% of the time
What They're Looking For.
Must Have
8 + years of hands-on experience in post-silicon test, evaluation, characterization, or validation of ASICs or SoCs, Deep practical experience with mixed-signal and/or high-speed digital blocks, understanding of Silicon bring-up, understanding of Measurement instrumentation and data interpretation, understanding of ATE-based test and characterization flows, Proven ability to debug silicon issues with limited observability and noisy data, Working knowledge of applying ML techniques to real measurement data, Experience using or integrating AI-enabled tools in engineering workflows, Comfortable working in Python-based analysis environments, Ability to reason about data quality, bias, observability limits, and measurement noise, cross-functional communication able to translate between silicon, test, and software/AI domains, Demonstrated technical leadership as a senior IC, Comfortable operating across abstraction levels—from waveform-level analysis to system-level implications
Nice to Have
Experience with adaptive or data-driven characterization, yield learning, or post-silicon tuning, Exposure to reinforcement learning, Bayesian optimization, or active learning concepts applied to engineering problems, Familiarity with RF test and characterization, Experience working with or evaluating Lab automation platforms, Experience working with or evaluating Test data analytics platforms, Experience working with or evaluating AI-assisted debug or analysis tools, Participation in industry conferences, standards groups, or technical publications related to silicon validation or test
What You'll Do.
Lead application of AI-enabled techniques to post-silicon test
Lead application of AI-enabled techniques to evaluation
Lead application of AI-enabled techniques to characterization
Strategically apply AI/ML and agentic tools to accelerate workflows
Strategically apply AI/ML and agentic tools to scale workflows
Strategically apply AI/ML and agentic tools to improve workflows
Define how AI is practically used in evaluation
Define how AI is practically used in characterization
Define how AI is practically used in calibration
Define how AI is practically used in trimming
Define how AI is practically used in qualification
Identify high-leverage opportunities where AI/ML can improve post-silicon workflows
Apply and integrate existing AI/ML technologies
Serve as a technical bridge between silicon/test engineers
Evaluate and prototype AI-enabled tools for post-silicon
Develop reference workflows for AI-assisted evaluation
Develop reference workflows for AI-assisted characterization
Develop guidelines for AI-assisted evaluation
Develop guidelines for AI-assisted characterization
Develop examples for AI-assisted evaluation
Develop examples for AI-assisted characterization
Create reusable frameworks that scale across products
Create reusable frameworks that scale across nodes
Create reusable frameworks that scale across business units
Mentor engineers on modern post-silicon data analysis techniques
Mentor engineers on practical use of AI tools in the lab
Mentor engineers on practical use of AI tools on ATE
Represent the organization in Technical reviews
Represent the organization in Vendor engagements
Represent the organization in Industry forums
How You'll Work.
Team & Collaboration
Serve as a technical bridge between silicon/test engineers and internal ML specialists; Serve as a technical bridge between silicon/test engineers and external vendors; Serve as a technical bridge between silicon/test engineers and academic or ecosystem partners; cross-functional communication able to translate between silicon, test, and software/AI domains; Driving initiatives without direct authority; Influencing methodology and direction
Communication Scope
cross-functional communication able to translate between silicon, test, and software/AI domains
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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