About the Role
We are seeking an experienced specialist with deep expertise in lithography and etch materials and processes to join us as a core domain expert. You will work hand-in-hand with AI engineers and product teams to build and maintain standardized databases of process and materials intelligence, designed for AI compatibility, for semiconductor reverse engineering.
This is a high-impact technical role with clear accountability. You will spend the majority of your time analyzing advanced material patents, academic literature, and technical intelligence to reconstruct material formulations, extract structured process parameters, and architect data systems that power our AI Agent. This role focuses on AI product development centered on data architecture and domain knowledge transformation.
Key Responsibilities
- Domain Authority: Serve as the domain authority for lithography and etch materials within our semiconductor reverse engineering AI products.
- Formulation & Tech Analysis: Deeply analyze advanced material patents, academic literature, and technical data to reconstruct material formulations, process parameters, and performance characteristics; extract structured, comparable technical data.
- Template Architecture: Design and maintain unified data extraction templates and field specifications to ensure collected data meets industrial standards.
- Data Engineering (Domain Level): Clean, normalize, and standardize process and materials data; build database frameworks optimized for AI model training and reasoning.
- Data Governance: Establish data entry specifications, quality verification criteria, and version control rules to ensure data consistency, traceability, and long-term usability.
- Comparative Insights: Conduct comparative analysis of materials and process data to identify technical traits, performance disparities, and process trade-offs.
- Cross-functional Knowledge Transfer: Deliver data insights and domain logic to AI and product teams, powering intelligent search, material benchmarking, and process recommendation functions.
- Tech Tracking: Track cutting-edge technologies and global market updates in lithography and etch materials; continuously enrich industry data resources.
- Framework Ownership: Own materials domain data management, translating sophisticated technical logic into clear guidelines for algorithm and product teams.
- Quality Assurance: Participate in data verification and AI function performance assessment, ensuring underlying data reliability from a materials science perspective.
Requirements
- Education: Bachelor’s degree or higher in Polymer Materials, Chemical Engineering, Materials Science, or related disciplines; solid academic foundation.
- Industry Experience: 3+ years of practical experience in photoresist, etch materials, or related fields, including but not limited to materials R&D, process development, failure analysis, technical intelligence, or data research.
- Domain Knowledge: Thorough understanding of complete lithography and etch process flows and material systems; capable of independently evaluating material properties and their process correlations.
- Data & Logical Thinking: Rigorous logical thinking with strong data awareness; able to translate complex research experience into standardized, structured data.
- Communication: Clear, effective cross-functional communicator; capable of aligning data requirements and transferring domain knowledge with global team members.
- Note on Coding: No advanced programming or software development experience (e.g., Python, SQL) is required. Your core value lies in your deep materials expertise and your unique ability to abstract complex scientific logic into structured data templates.
Preferred Qualifications
- Background in process, materials, or data analysis at leading semiconductor manufacturers or top-tier material suppliers is highly desirable.
- Experience developing and maintaining materials databases, process parameter libraries, or technical knowledge repositories.
- Hands-on experience in patent analysis, material teardown, or failure analysis.
- Familiarity with materials informatics tools, database management, or data annotation workflows.
- Genuine curiosity about AI-enabled materials research and the digital application of process data.