We are looking for a Senior Silicon Validation and Methodology Engineer specializing in In System Testing to join our dynamic team. This role will be pivotal in ensuring the validation and performance of our silicon designs, working closely with various cross-functional teams to refine and improve our methodologies.
Responsibilities:
- Lead the development and execution of validation plans for in-system testing, ensuring rigorous assessment of silicon performance and reliability.
- Collaborate with design, verification, and architecture teams to define validation methodologies and criteria, integrating best practices into workflows.
- Develop automated test setups and scripts to ensure efficient and repeatable testing processes, improving coverage and accuracy.
- Analyze test results, identify anomalies, and provide actionable insights to engineering teams to drive design improvements.
- Mentor junior engineers in validation methodologies and best practices, fostering a collaborative and continuous learning environment.
Requirements:
- BS or MS in Electrical Engineering, Computer Engineering, or related field, or equivalent experience.
- 5+ years of experience in silicon validation or a related role, with a strong focus on in-system testing methodologies.
- Proficient in scripting languages such as Python or Perl, and experience with automation frameworks.
- Strong understanding of silicon architecture, hardware design, and testing methodologies.
- Excellent problem-solving skills and the ability to work effectively in cross-functional teams.
- Strong AI-enabled skills and thinking , use AI to accelerate analysis, exploration, and documentation while maintaining rigor, originality, and judgment that do not come from AI.
Benefits:
- Experience with advanced testing techniques, including ATE (Automatic Test Equipment) and custom hardware setups.
- Familiarity with simulation and modeling tools in the context of silicon validation.
- Understanding of machine learning applications in silicon testing and validation processes.
- Ownership of hard problems, deep work, and AI-enabled engineering at scale.
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