MTS Data Scientist, Agentic Platforms

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’s Data Science experts develop AI and data-driven solutions to tackle hard to solve challenges in semiconductor process and fab technology development. The team plays a key role in enabling intelligent automation and decision‑making across Micron’s portfolio

• Own the creation of AI systems optimizing semiconductor design, product engineering, verification & validation, and manufacturing workflows using large‑scale data.
• Architect and implement Agentic AI systems that integrate with Modeling tools, EDA tools, design environments, and product/manufacturing test platforms to automate tasks such as spec translation, design verification, product validation, and test log root cause analysis for components and system‑level products.
• Establish and promote Best Known Methods (BKMs) for deploying LLMs and agentic systems in live environments, ensuring reliability, efficiency, and maintainability.
• Benchmark and evaluate model performance using structured evaluation frameworks, and continuously refine models through timely tuning, RLHF, and feedback loops.
• Collaborate with multi‑functional teams—including process engineers, design engineers, product and test engineers, and data scientists—to define high‑impact problem statements and deliver scalable AI solutions.
• Communicate technical insights and solution strategies clearly to both technical and non‑technical team members through compelling data storytelling and visualizations.

• Must have a Master’s, PhD, or equivalent experience in Electrical Engineering, Computer Science, or a related field with 10+ years of demonstrated experience
• Proficient in Python, with five years of experience working with a variety of semiconductor design & process datasets. Proficient in using enterprise data platforms like Snowflake, Big Query, MSSQL, Oracle, and AWS Redshift for scalable data processing and analysis.
• Hands‑on experience building and leading AI/ML projects involving LLMs, RAG, and Agentic workflows deployed, demonstrated expertise in ML frameworks such as PyTorch or Tensor Flow.
• Solid understanding of domain‑adapted LLM training with practical experience, including pretraining, post training on in‑domain synthetic datasets, and model quantization for efficient deployment
• Must have shown cloud platform experience such as GCP, AWS, or Azure, including deployment of ML pipelines in production environments.
• Familiarity with LLM evaluation and benchmarking techniques, including RLHF, timely tuning, and reward modeling for hardware‑aware use cases.
• Consistent track record to independently drive AI/ML projects from problem prioritization through deployment in semiconductor environments.
• Excellent interpersonal experience with the ability to collaborate with process technology, silicon design, design verification, validation, and product engineering teams, and translate sophisticated AI/ML concepts into actionable solutions for hardware development.

• Experience includes direct application of AI/ML to semiconductor design, processing, 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 evaluation tools (e.g., Agent Eval).
• Shown proficiency in translating sophisticated technical concepts into actionable insights for multi‑functional teams!

As a world leader in the semiconductor industry, Micron is dedicated to your personal wellbeing and professional growth. Micron benefits are designed to help you stay well, provide peace of mind and help you prepare for the future. We offer a choice of medical, dental and vision plans in all locations enabling team members to select the plans that best meet their family healthcare needs and budget.

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