Manager, STPG PE NAND

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE LTD; Região Norte; Presencial; BRL 400 000 - 700 000; Tempo integral

Resumo da oferta

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE LTD is seeking a senior Probe Engineering leader to drive cross-functional initiatives across design, fabrication, and test. You will guide teams through silicon characterization, yield learning, and NPI ramps, while maintaining a data-driven culture.

You will own probe coverage strategy, enable first silicon bring-up, and champion AI/ML driven test optimization and automation to boost productivity and product quality across global sites.

Qualificações

• 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.

Responsabilidades

• 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.
• 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.

Conhecimentos

People leadership

Team development

AI adoption

Data analytics

Probe engineering

Cross-functional collaboration

Formação académica

Bachelor's/Master's/PhD in Electrical/Electronics Engineering or related field

Ferramentas

DFT

Descrição da oferta de emprego

Key Responsibilities

People Leadership & Team Development

• 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.

Probe Coverage Strategy & Ownership

• 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.

Cross-Functional Leadership & Stakeholder Management

• 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.

First Silicon Bring-Up & Yield Enablement

• 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.

Data, Analytics & AI Enablement

• 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.

AI Adoption & Efficiency Leadership

• 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.