What are the responsibilities and job description for the Sr Data Scientist, Agentic Platforms position at Micron Technology?
Develop AI solutions to improve semiconductor design and manufacturing workflows. Collaborate with different teams to deliver effective AI systems. Must have a Master's, PhD, or equivalent experience in Electrical Engineering, Computer Science, or a related field. Bringing to bear expertise in Python, with proven experience handling various semiconductor design & process datasets. Proficient in using enterprise data platforms like Snowflake, BigQuery, 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 in semiconductor environments, demonstrated expertise in ML frameworks such as PyTorch or TensorFlow. 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, timely tuning, and reward modeling for hardware-aware use cases. Proven ability to independently drive AI/ML projects from problem prioritization 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, yield analysis, 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., LangGraph, AutoGen) and evaluation tools (e.g., AgentEval). Demonstrated ability to translate complex technical concepts into actionable insights for cross-functional teams.
