Sr Staff Product Engineer

Renesas Electronics•Morrisville, NC

17h•Hybrid

About The Position

Renesas is a global leader in semiconductor solutions, enabling innovations across industrial, IoT, edge computing, and intelligent power applications. Our teams drive the development of next-generation products, including devices supporting Edge AI capabilities, by collaborating across design, product engineering, and manufacturing to deliver high-quality, scalable solutions worldwide.

Requirements

Applicants for this position must be currently authorized to work in the United States on a full-time basis. Renesas is unable to sponsor applicants for work visas for this position now or in the future.
Min Education: Bachelor’s of Science in Electrical or Microelectronics Engineering
10+ years of experience with a Bachelor’s degree
8+ years with a Master’s degree
5+ years with a PhD
Proven success in releasing multiple semiconductor products from concept through qualification and into HVM.
Deep expertise in statistical analysis, including distribution analysis, correlation analysis, and limit optimization.
Strong background in product characterization and electrical performance evaluation.
Extensive experience in reliability qualification, ESD, and latch-up methodologies.
Demonstrated expertise in failure analysis and root cause investigation across multiple failure modes.
Proven leadership in solving highly complex technical problems using data-driven approaches.
Experience influencing engineering decisions and leading cross-functional technical initiatives.

Nice To Haves

Strong working knowledge of JEDEC standards (e.g., JESD47, JESD22 series) and industry qualification practices.
Hands-on experience with ESD qualification (HBM, CDM) and latch-up testing/analysis.
Experience with reliability stress planning and interpretation (HTOL, HAST, TC, ELFR, etc.).
Experience with Edge AI-enabled products or data-centric semiconductor applications.
Familiarity with applying machine learning or AI techniques to engineering data analysis workflows.
Proficiency with JMP, Python, or other advanced data analysis and visualization tools.
Demonstrated success driving efficiency improvements in characterization, qualification, and yield analysis workflows.

Responsibilities

Lead end-to-end product lifecycle execution across multiple programs—from concept definition through characterization, qualification, customer release, and ramp to high-volume manufacturing (HVM).
Define and drive product validation, characterization, and qualification strategies aligned with product requirements, reliability expectations, and customer use cases.
Demonstrate a proven track record of successfully releasing multiple IC products into production and sustaining performance through volume ramp.
Apply advanced statistical analysis and data science techniques to characterize device electrical performance and parametric behavior.
Develop robust methodologies for analyzing distributions, corner performance, and guard band optimization.
Lead deep-dive investigations of yield excursions, parametric shifts, and failure mechanisms using structured statistical approaches and large-scale data analysis.
Identify correlations across design, silicon, and test datasets to uncover root causes and improve product robustness.
Establish scalable analytics frameworks, dashboards, and visualization tools to enable data-driven decision making across product lifecycle phases.
Define and execute comprehensive product qualification strategies aligned to JEDEC and industry standards (e.g., JESD47, JESD22 series).
Drive reliability stress planning and interpretation, including HTOL, HAST/uHAST, TC, ELFR, and associated qualification methodologies.
Lead ESD and latch-up qualification strategy, data analysis, and failure resolution in alignment with product requirements.
Analyze reliability data to assess failure mechanisms, lifetime projections, and margin to specification limits.
Ensure qualification coverage, sample sizes, and stress conditions support defensible product release decisions.
Partner with reliability and quality teams to resolve qualification risks and define mitigation strategies.
Lead complex failure analysis activities across electrical, parametric, ESD, latch-up, and reliability-related failures.
Utilize data-driven approaches to correlate failure signatures with design, process, or test-related mechanisms.
Drive cross-functional root cause investigations and ensure corrective actions are implemented and verified.
Develop systematic approaches to failure classification, screening effectiveness, and defect pareto analysis.
Identify and drive opportunities to improve engineering efficiency through application of AI, machine learning, and advanced analytics in areas such as: Characterization data reduction and automation, Anomaly detection and outlier classification, Predictive yield and reliability modeling.
Develop or leverage intelligent workflows to accelerate insight generation and reduce manual analysis effort.
Promote adoption of data-centric and AI-assisted methodologies to improve engineering productivity and decision quality.
Serve as a recognized subject matter expert in product engineering, statistical analysis, reliability, and failure analysis.
Lead cross-functional efforts across design, applications, reliability, and test teams to resolve highly complex technical challenges.
Provide leadership in defining characterization plans, qualification strategies, and analysis methodologies.
Mentor engineers in advanced statistical techniques, reliability interpretation, and structured problem solving.
Work on complex, ambiguous problems requiring evaluation of incomplete or conflicting data, applying conceptual and statistical thinking to determine optimal solutions.
Anticipate technical risks in product performance, qualification adequacy, and reliability margins, and proactively drive improvements.
Contribute to development of best practices in qualification methodology, data analysis, and engineering decision frameworks.
Build and lead networks across global teams to align characterization strategy, qualification coverage, and product readiness.
Communicate complex analytical findings, qualification results, and failure analysis conclusions to diverse stakeholders, including senior leadership.
Influence product release decisions through data-driven insight, technical expertise, and sound engineering judgment.
Act as a key authority on product readiness, with accountability for decisions impacting product quality and business outcomes.