Staff Engineer, Advanced Modeling & AI Solutions

Overview

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.

Micron Technology’s vision is to transform how the world uses information to enrich life. Our dedication to people, innovation, tenacity, partnership, and customer focus allows us to fulfill our mission to be a global leader in memory and storage solutions. This means conducting business with integrity, accountability, and collaboration while supporting our global community.

About the Team

Micron’s Technology Development experts develop data driven and physics based Advanced Modeling and AI solutions to tackle complex challenges in semiconductor component, packaging, and systems engineering. The team plays a key role in predictive modeling, intelligent automation, and decision-making across Micron’s memory and storage portfolio.

• Drive the deployment of intelligent, advanced physics or ML-powered modeling solutions that enhance semiconductor process technology development, package development, design, and early manufacturing workflows—leveraging large-scale, unstructured and often sparse data to deliver robust, domain-specific AI systems from problem definition to production.

• Architect and implement agentic AI systems that integrate with Advanced Modeling tools, design automation tools / environments, and product/fab/manufacturing test platforms to accelerate digital twin development for fab technology co-optimization covering wafer, die, and package level models across tech nodes.

• Establish and promote Best Known Methods (BKMs) for deploying LLMs and agentic systems in production environments, ensuring reliability, efficiency, and maintainability.

• Benchmark and evaluate model performance using structured evaluation frameworks, and continuously refine models through prompt tuning, RLHF, and feedback loops.

• Accelerate insight generation from predictive modeling, images, and data. Communicate technical insights and solution strategies clearly to both technical and non-technical stakeholders through compelling data storytelling and visualizations.

• Must have a Master’s or PhD in Electrical Engineering, Computer Science, or a related field.

• Proficient in Python, with over 5 years of experience working with a variety of semiconductor design & process datasets. Proficient in leveraging enterprise data platforms such as Snowflake, Big Query, MSSQL, Oracle, and AWS Redshift for scalable data processing and analysis.

• Hands-on experience building and managing AI/ML projects involving LLMs, RAG, and agentic workflows deployed in semiconductor environments, demonstrated expertise in ML frameworks such as PyTorch or Tensor Flow.

• Strong knowledge of domain-adapted LLM training with practical experience, including pretraining, post training on in-domain synthetic datasets, and model quantization for efficient deployment in semiconductor environments.

• Must have experience working with cloud platforms such as GCP, AWS, or Azure, including deployment of ML pipelines in production environments.

• Familiarity with LLM evaluation and benchmarking techniques, including RLHF, prompt tuning, and reward modeling for hardware-aware use cases.

• Proven ability to independently drive AI/ML projects from problem scoping through deployment in semiconductor environments.

• Excellent communication skills with the ability to collaborate with process technology, silicon design, design verification, validation and product engineering teams, and translate complex AI/ML concepts into actionable solutions for hardware development.

• Experience includes direct application of AI/ML to semiconductor design, processing, verification, validation, and manufacturing workflows.

• Deep understanding of semiconductor-specific AI/ML applications,

• Experience with CI/CD pipelines and MLOps practices for ML/LLM deployment.

• Strong understanding of agentic AI frameworks (e.g., Lang Graph, Auto Gen) and…