Role Overview:
As an Application Engineer for the V93000 SoC ATE system, you will support our global customer base in developing, optimizing, and troubleshooting semiconductor test programs while also playing a key role in enabling AI/LLM capabilities within our software ecosystem. This hybrid role combines deep semiconductor test expertise with modern AI engineering: developing test methodologies, supporting customer projects, and collaborating on integration of LLM-driven tools that enhance productivity, test automation, and engineering workflows across SmarTest 8 and related platforms.
You will bridge the worlds of ATE application engineering and AI-assisted engineering, contributing to next-generation smart tooling that improves test quality, reduces debug time, and unlocks intelligent automation for semiconductor development and production.
Key Responsibilities:
- Provide application development support for V93000 SoC ATE systems, including test program creation, debug, and optimization in context of AI project
- Communicate with customers to understand requirements and ensure successful delivery
- Collaborate with global field engineers, R&D, and application development teams
- Support the integration of AI capabilities into SmarTest 8 and related test development flows
- Collaborate with R&D on the design and validation of LLM-assisted engineering tools, including code generation, error resolution, and automated test documentation
- Assist in dataset preparation, data extraction, and domain-specific curation for LLMs
- Contribute to the evaluation of AI features using semiconductor test scenarios (test programs, debug logs, parametric data)
- Work with Java/C++ and Eclipse-based environments to integrate AI components into existing toolchains
- Provide feedback from real customer use-cases to guide improvement of AI-enabled features
- Help ensure AI tools meet requirements for correctness, robustness, latency, and usability within semiconductor test workflows
- Support internal testing of RAG pipelines, embeddings, and model behaviors in the context of V93000 workflows