Qorvo (Nasdaq: QRVO) supplies innovative semiconductor solutions that make a better world possible. We combine product and technology leadership, systems-level expertise and global manufacturing scale to quickly solve our customers' most complex technical challenges. Qorvo serves multiple high-growth segments of large global markets, including consumer electronics, smart home/IoT, automotive, EVs, battery-powered appliances, network infrastructure, healthcare and aerospace/defense. Visit to learn how our innovative team is helping connect, protect and power our planet.
About the Role- We are seeking an experienced Control Systems Architect to join the Texas Fab Yield Enhancement and Control Systems team. This person will be a solutions srchitect to design and deploy intelligent systems that drive yield improvement with root cause analysis and process optimization across our wafer fabrication operations. You will bridge advanced data analytic techniques with deep semiconductor manufacturing domain knowledge to build production-grade platforms.
Key Responsibilities- Architect and implement control systems for automated root cause analysis of yield excursions and process drift
- Design statistical correlation engines linking process parameters, metrology data, and electrical test results across fabrication steps
- Build and maintain AI/ML solutions for anomaly detection and process control enhancement
- Work with IT to integrate solutions with manufacturing systems and data platforms.
- Lead cross functional teams of Yield, Process, Equipment, Manufacturing, and IT teams to build and deploy production software applications.
- Define and help manage with IT the site data architecture strategies for optimal impact.
Required Qualifications- Bachelor's Degree in Electrical Engineering, Materials Science, Computer Science, or related field - or equivalent industry experience.
- 5+ years in semiconductor manufacturing, process engineering, or fab data systems
- If Master's or PhD degree less years industry experience may be considered.
- Expertise in statistical correlation methods (multivariate analysis, PCA, partial correlation, Granger causality) applied to manufacturing data
- Strong software engineering skills (Python, SQL, Spark/PySpark) with understanding of production-quality code practices
- Demonstrated experience with AI architectures
- Deep understanding of ML techniques: supervised/unsupervised learning, time-series analysis, Bayesian methods, and causal inference
- Familiarity with yield management concepts: bin pareto, wafer maps, lot genealogy, split-lot analysis, inline-to-final correlation
Preferred Qualifications- Strong background in SPC, FDC, APC systems in semiconductor fab environment
- Experience with semiconductor processes and integration for device fabrication
- Working knowledge of MES/EMS systems, SECS/GEM, and equipment data collection
- Hands-on experience with advanced data storage, data engineering, and advanced data analytic techniques.
- Familiarity with LLM frameworks
Education- BS / MS / PhD in Electrical Engineering, Materials Science, Computer Science, or related field - or equivalent industry experience
This position is not eligible for visa sponsorship by the Company.
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MAKE A DIFFERENCE AT QORVO
We are Qorvo. We do more than create innovative RF and Power solutions for the mobile, defense and infrastructure markets - we are a place to innovate and shape the future of wireless communications. It starts with our employees. As a unified global team, we bring a commitment to excellence, growth and a passion for creating what's next. Explore the possibilities with us.
We are an Equal Employment Opportunity (EEO) employer and welcome all qualified applicants. Applicants will receive fair and impartial consideration without regard to any characteristics protected by applicable law, including race, color, religion, sex (as defined by law), national origin, age, military or veteran status, genetic information, or disability.
Qorvo is an E-Verify Employer. For more information, please see the Right to Work and E-Verify Participation posters.
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