Manager, STPG PE NAND

Sep 21, 2026 Sep 25, 2026 2 views

Key Responsibilities

Recruit, develop, and retain top engineering talent; build a diverse, high-performing team.
Set clear expectations, provide regular feedback, and conduct performance reviews aligned to business outcomes.
Coach engineers on technical depth, problem-solving, and cross-functional collaboration skills.
Foster a culture of ownership, continuous learning, and data-driven decision-making.
Manage team capacity, priorities, and workload across concurrent projects and technology ramps.
Champion AI tool adoption within the team; enable engineers to leverage AI assistants and automation for accelerated productivity.

Own and drive probe coverage strategy across the product portfolio, ensuring alignment with design intent, process risks, and customer requirements.
Establish team standards for probe limits, guard-bands, and screening mechanisms based on silicon characterization.
Lead probe enablement for new product introductions (NPI) and technology ramps; identify risks early and drive mitigation.
Define and release probe test flows from first silicon through qualification and HVM, balancing coverage with test efficiency.

Partner with Design, DFT, Design Validation (DV), Process Integration, Backend Test, and Reliability teams to ensure probe solutions are technically sound and scalable.
Represent Probe Engineering in cross-functional forums; influence DFT architecture, test hooks, and observability decisions.
Drive alignment between probe strategy and downstream test requirements; ensure seamless handoffs.
Communicate team progress, risks, and trade-offs to senior leadership with clarity and data-backed recommendations.

Oversee first-silicon bring-up activities; ensure team delivers timely characterization and yield learning.
Drive structured root-cause analysis, failure-mode investigation, and feedback loops to Fab, PI, and Design teams.
Enable yield ramp through data-driven probe optimization and coverage right-sizing.

Champion AI/ML initiatives such as predictive probe, smart sampling, anomaly detection, and test optimization.
Drive adoption of data analytics and automation tools to improve probe effectiveness and team efficiency.
Identify opportunities to streamline workflows and enable data-driven decision-making across the team.

Lead and own AI efficiency projects that transform engineering workflows, targeting measurable productivity gains across the team.
Drive strategic adoption of generative AI tools (e.g., coding assistants, documentation automation, data analysis copilots) to accelerate engineering deliverables.
Establish AI adoption roadmaps and success metrics; track and report efficiency improvements to leadership.
Identify high-impact use cases for AI automation in probe engineering processes, from test program development to failure analysis.
Partner with IT and AI/ML platform teams to pilot and scale AI solutions; provide feedback to shape enterprise AI strategy.
Build team capability in AI-assisted workflows through training, best practices, and hands-on enablement.
Ensure responsible AI usage aligned with data governance, IP protection, and quality standards.

Required Qualifications

Bachelor's, Master's, or PhD in Electrical/Electronics Engineering, Computer Engineering (with hardware/semiconductor focus), Semiconductor Physics, or related field.
5+ years of experience in semiconductor product engineering, wafer test, or related discipline.
2+ years of experience leading or mentoring engineers; formal people management experience preferred.
Strong fundamentals in semiconductor devices, silicon characterization, yield mechanisms, and probe/test strategy.
Demonstrated ability to translate business objectives into team goals and drive execution.
Proven track record of cross-functional collaboration and stakeholder influence.
Strong analytical skills with ability to guide structured root-cause analysis and data-driven decisions.
Excellent communication skills; ability to lead across global, cross-functional teams.
Demonstrated experience driving technology adoption or process improvement initiatives.

Preferred Qualifications

Experience with DFT, design-to-manufacturing integration, or backend test operations.
Familiarity with AI/ML applications in test optimization, predictive analytics, or smart manufacturing.
Track record of driving process improvements, test cost reduction, or yield enhancement initiatives.
Experience leading AI adoption or digital transformation projects; hands-on familiarity with AI-assisted engineering tools.
Track record of delivering measurable efficiency gains through automation or AI-enabled workflows.

Skills