Staff Engineer, Advanced Modeling & AI Solutions

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, BigQuery, 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 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, 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., LangGraph, AutoGen) and evaluation tools (e.g., AgentEval). Demonstrated ability to translate complex technical concepts into actionable insights for cross-functional teams.