Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. You will drive measurable outcomes in labor productivity and annualized hour savings by implementing strategies focused on standardization, simplification, automation, and leveraging generative AI methodologies. These approaches will enable reductions in labor hours, enhance labor efficiency, shorten cycle times, and improve overall operating cadence. Responsibilities Program Management and Delivery - Drive year over year improvement in Frontend (FE) labor productivity VAM/HC in support of the 5MORE productivity roadmap. - Lead E2E execution and governance of FE labor productivity programs to ensure on time delivery and measurable outcomes. Standardize tools, methods, and processes across sites to enable scalable and repeatable execution aligned with enterprise goals. - Integrate labor initiatives across AI, automation, robotics, DREAM team, and innovation programs into a cohesive execution plan. Track, validate, and report productivity benefits including labor hour reduction, VAM/HC, and annualized savings in partnership with Site's Labor Champions & Finance team. - Maintain and optimize the loading-based staffing (LBS) model to align staffing levels with actual workload demand. - Provide governance support for SEAL and SWAT initiatives. - Lead Labor SWAT initiatives to improve FE labor productivity and functional AI roadmap. Targeted actions address gaps, accelerate progress, and support overall productivity goals. FE Network Productivity and Enablement - Monitor labor performance metrics, identify gaps, benchmark best practices, and support performance recovery actions when needed. - Enable cross site knowledge sharing to scale successful solutions and maximize network impact. - Guide site and central teams to identify and prioritize productivity opportunities using AWMS workload data to convert effort into measurable time savings. - Managing programs involving pipelines of AI, GenAI, automation, and low code solutions, including copilots, digital footprint, and workflow automation. - Support development and execution of functional AI roadmaps across FE organizations with focus on reuse and scalability. - Drive structured use case intake, value assessment, feasibility review, and deployment planning to ensure sustainable adoption. Data Driven Insights and Reporting - Develop KPI dashboards and management reporting to provide visibility into progress, adoption, and realized benefits. - Translate data into actionable insights to support fact-based prioritization and decision making. - Support workload forecasting, productivity baselining, and scenario modeling by coordinating requirements and validating outputs with stakeholders. Requirements - Degree in Engineering, Industrial Engineering, Statistics, or a related field - At least 3+ years of experience in semiconductor operations. - Strong program management skills (portfolio planning, governance, benefits realization, dependency management, change management). - Preferred experience leading programs/projects with demonstrated outcomes in productivity, operational efficiency, or workforce/labor optimization. - Working knowledge of GenAI concepts and delivery (use-case framing, prompt/workflow design basics, evaluation of value and risk, adoption metrics). - Experience delivering automation and low-code solutions (e.g., Power Automate, Copilot Studio, UiPath, SQL or similar), including requirements-to-deployment lifecycle. - Strong analytical capability (KPIs, dashboards, baseline/benefits tracking); able to translate data into decisions and execution plans. - Excellent communication and stakeholder management skills across site, central, and executive audiences. - Proficiency with Microsoft 365 (Excel/PowerPoint/Teams) and data visualization tools (e.g., Power BI). About Micron Technology, Inc. We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all . With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities - from the data center to the intelligent edge and across the client and mobile user experience. To learn more, please visit micron.com/careers All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. To request assistance with the application process and/or for reasonable accommodations, please contact hrsupport_japan@micron.com Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards. Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron. AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification. Fraud alert: Micron advises job seekers to be cautious of unsolicited job offers and to verify the authenticity of any communication claiming to be from Micron by checking the official Micron careers website in the About Micron Technology, Inc.

• Own the Training Environment data architecture end-to-end: dataset design and schema for all ML training pipelines, including dialog corpora for LLM training, conversational steps for NLU models, annotated evaluation sets, and whole-call recordings for speech-to-speech model development. • Define and govern data selection and sampling strategy: establish criteria that determine which production conversations have the highest training value, including diversity-optimized sampling, confidence-based filtering, edge-case prioritization, and deduplication strategies. • Build and maintain the data catalog and dataset discovery infrastructure: enable ML engineers across LLM, NLU, Speech, and Agentic teams to find, understand, and use training data without friction. • Define annotation pipeline architecture: establish requirements for data labeling — intent annotation, entity tagging, dialog act classification, task completion scoring, and agentic reasoning evaluation — across internal annotators and external vendors. • Architect the data flywheel: the closed-loop system where real customer conversations feed back into training data collection, curation, annotation, model retraining, and evaluation. • Own and maintain data pipelines and infrastructure spanning Snowflake, AWS S3, ETL/ELT pipelines (Airflow), and integration with ML training workflows on AWS SageMaker. • Work directly with LLM, NLU, and Agentic systems teams to understand training data requirements — what conversational patterns improve zero-shot routing accuracy, what dialog structures train better task planners, what edge cases stress-test agentic reasoning — and translate these into concrete dataset specifications and pipeline configurations. • Define and maintain the data architecture for Omilia's Training Environment: schema design, data flow patterns from production (OCP) to centralized training infrastructure, storage strategy (Snowflake + S3), cross-pipeline consistency, and clear auditable data lineage, including anonymization requirements as part of the compliance layer. • Design data quality frameworks that directly improve model outcomes: content-based deduplication, diversity-maximizing sampling, confidence-based filtering using NLU scores and behavioral signals, and dedicated NLU improvement corpus extraction from low-confidence and no-match production data. • Define annotation requirements for ML model development — intent labeling guidelines, entity tagging schemas, dialog act classification, task completion scoring, and reasoning quality assessment — and design annotation workflows that produce consistent, high-quality labels at scale; evaluate and manage external data annotation vendors. • Build and maintain the data catalog that enables cross-team dataset discovery: document dataset contents, schemas, lineage, quality metrics, intended use cases, and known limitations; define the taxonomy for organizing training datasets across model types (LLM, S2S, NLU, ASR, TTS, agentic). • Architect the closed-loop data flywheel: production conversations → data selection → anonymization → curation → annotation → model training → evaluation → safe redeployment → back to production; define feedback mechanisms that route model failure cases into targeted training data collection. • Identify gaps in production training data and define requirements for external data acquisition (public datasets, synthetic data generation, vendor-sourced corpora); design data augmentation strategies for underrepresented languages, domains, or conversational patterns. • Work closely with LLM/NLU/S2S/ASR/TTS/VB Tech Leads and Senior Engineers to align data architecture with model training requirements; collaborate with Platform Engineering, Security & Compliance, and Product Management stakeholders. • Maintain comprehensive documentation of data architecture, dataset specifications, pipeline configurations, and data catalog; produce data architecture RFCs for significant changes and share best practices with ML teams.

• Coach and mentor a caseload of learners through 1:1 sessions and workshops • Teach practical data engineering concepts in a way that’s clear, relevant, and grounded in real‑world experience • Help learners design and build reliable, scalable, secure data solutions - not just working code • Guide learners through assessments and portfolios with high standards and supportive feedback • Model strong professional judgement around performance, cost, security, privacy, and ethics

• Design, build, and maintain efficient data pipelines (ETL processes) to integrate data from various source systems into the data warehouse. • Develop and optimize data warehouse schemas and tables to support analytics and reporting needs. • Write and refine complex SQL queries and use scripting (e.g., Python) to transform and aggregate large datasets. • Implement data quality measures (such as validation checks and cleansing routines) to ensure data integrity and reliability. • Collaborate with data analysts, data scientists, and other engineers to understand data requirements and deliver appropriate solutions. • Document pipeline designs, data flows, and data definitions for transparency and future reference, adhering to team standards. • Handle multiple tasks or projects simultaneously, prioritizing work and communicating progress to stakeholders to meet deadlines.