Staff Product Engineer (GenAI, AI/ML & Advanced Data Analytics)

Micron Technology, Inc; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Micron Technology, Inc. in Singapore is seeking a Staff Product Engineer to lead GenAI, AI/ML, and advanced data analytics initiatives for semiconductor engineering.

You will prototype and scale AI‑driven solutions to improve productivity, decision making, and insights across manufacturing and validation workflows. This role partners with design engineering, data science, IT, and manufacturing to deliver end‑to‑end AI systems, from data pipelines to production deployment, with emphasis on

Qualifications

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Data Science, Statistics, AI or related field.
• Minimum 2 years of hands‑on experience developing and deploying AI applications in engineering contexts.
• Strong programming proficiency in Python and SQL.
• Solid foundation in data analytics and visualization using pandas, scikit‑learn, matplotlib, plotly.
• Familiarity with agentic AI frameworks and evaluation tools.
• Experience with modern AI coding tools and agentic coding harnesses.
• Hands‑on experience deploying AI/ML systems involving LLMs, RAG, and production frameworks.
• Cloud experience with GCP/AWS/Azure and deploying ML pipelines in production.

Responsibilities

• GenAI system development for semiconductor engineering workflows.
• Develop scalable data pipelines to ingest, clean, transform, and analyze large datasets.
• Apply data science libraries to perform deep analysis and generate visualizations.
• Build, evaluate, and optimize LLM‑based workflows and RAG pipelines.
• Develop and productionize ML/DL models for classification, anomaly detection, and decision support.
• Implement robust data processing techniques in distributed frameworks.
• Collaborate with cross‑functional teams to translate engineering problems into AI/ML solutions.
• Support deployment, monitoring, and operationalization of AI/ML solutions in cloud environments.

Skills

Python

SQL

Data analytics

Pandas/Matplotlib

LLM/GenAI

Cloud experience

PyTorch/TensorFlow

Kubernetes

Communication

Education

Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Data Science, Statistics, AI or related field

Tools

BigQuery

Snowflake

MSSQL

Oracle

Redshift

Kubernetes

LangGraph

Google ADK

AutoGen

Job description

Req. ID: JR99439

Staff Product Engineer (GenAI, AI/ML & Advanced Data Analytics)

As part of the HIG HBM Product Engineering organization, you will help drive the development of next‑generation GenAI, machine learning, and advanced data analytics solutions for semiconductor engineering. In this role, you will work on intelligent systems that improve engineering productivity, strengthen technical decision‑making, and unlock insights from complex manufacturing, validation, and engineering workflows.

You will collaborate with cross‑functional teams across Product Engineering, Design Engineering, System Engineering, Data Science, IT, and Manufacturing to prototype, build, and scale practical AI‑driven solutions that improve quality, cost, cycle time, and engineering efficiency.

Key Responsibilities

• GenAI System Development: Design, build, and improve GenAI‑powered and agentic systems supporting semiconductor engineering workflows such as code generation, data extraction, analytics, documentation automation, failure triage, and technical knowledge retrieval.
• Large‑Scale Data Pipelines: Develop scalable data pipelines and analytical workflows to ingest, clean, transform, and analyze large, complex, and heterogeneous datasets from multiple manufacturing and engineering systems.
• Advanced Data Analytics: Apply Python, SQL, and data science libraries (pandas, matplotlib) to perform deep analysis, generate visualizations, and deliver actionable engineering insights.
• LLM Workflow Engineering: Build, evaluate, and optimize LLM‑based workflows, including prompting, retrieval‑augmented generation (RAG), inference orchestration, benchmarking, and quality evaluation.
• Machine Learning Production: Develop and productionize machine learning and deep learning models for classification, regression, anomaly detection, failure analysis, and engineering decision support.
• Distributed Data Processing: Implement robust data processing techniques such as data cleansing, outlier detection, and missing‑data handling using distributed or large‑scale frameworks (PySpark, BigQuery).
• Cross‑Functional Collaboration: Partner with domain experts and cross‑functional teams to translate complex engineering problems into scalable AI/ML and analytics solutions.
• Production Deployment: Support deployment, monitoring, and operationalization of AI/ML solutions in cloud and enterprise environments.
• Technical Communication: Communicate technical findings, recommendations, and model outcomes clearly to both technical and non‑technical stakeholders.
• Innovation Leadership: Identify and drive high‑impact opportunities where GenAI, machine learning, and analytics can improve engineering productivity and business outcomes.

Minimum Qualifications

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Data Science, Statistics, Artificial Intelligence, or a related field.
• Minimum 2 years of hands‑on experience developing and deploying AI applications in semiconductors, electronics, or other engineering industries.
• Strong programming proficiency in Python and SQL.
• Strong technical foundation in data analytics and visualization, including tools and libraries such as pandas, scikit‑learn, matplotlib, plotly, or similar ecosystems.
• Familiarity with agentic AI frameworks such as LangGraph, Google ADK, AutoGen and evaluation tools like AgentEval.
• Familiarity with modern AI coding tools / agentic coding harnesses, such as Claude Code, Roo Code, Cursor, Cline, Windsurf, Gemini CLI, or similar tools.
• Hands‑on experience developing and deploying AI/ML systems involving LLMs, including RAG, agentic workflows, and frameworks such as PyTorch or TensorFlow.
• Cloud experience with GCP, AWS, or Azure, including deploying ML pipelines in production.
• Experience with LLM training, inference, and evaluation workflows, including prompt design, benchmarking, validation, or retrieval‑augmented systems.
• Experience analyzing large, complex, and heterogeneous datasets from multiple systems and applying sound techniques for data cleansing, outlier handling, and missing‑data treatment.
• Strong analytical, problem‑solving, and software development skills.
• Strong communication skills with the ability to explain technical concepts and findings effectively.
• Strong sense of ownership, accountability, and engineering rigor.

Preferred Qualifications

• Experience building agentic systems or AI solutions for semiconductor manufacturing, product engineering, validation, yield improvement, reliability, or failure analysis.
• Deep understanding of semiconductor‑specific AI/ML applications.
• Demonstrated understanding of deep learning architectures and computer vision.
• Experience using enterprise data platforms such as BigQuery, Snowflake, MSSQL, Oracle, or Redshift.
• Experience with Kubernetes or similar production infrastructure and deployment frameworks.
• Experience with web application technologies such as JavaScript, HTML, and CSS.
• Experience designing scalable, enterprise‑grade AI/ML systems with attention to reliability, traceability, and operational readiness.
• Familiarity with document‑processing pipelines, technical knowledge platforms, or retrieval systems for engineering content.
• Experience working in cross‑functional environments spanning engineering, manufacturing, data science, and IT.

Job Profile(s): Product Development Engineer 4

Relocation level: (TBD)

Micron is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.