Spacex Yield Engineer at SpaceX focused on RF final test integration for Starlink/Akoustis RF filters. Responsible for developing RF test solutions, yield analysis, and process improvements in high-mix production.
Responsibilities
Develop and implement RF test cases and calibration procedures for BAW filter devices operating in the 2 GHz to >10 GHz range
Establish strong linkages between wafer probe data and final RF test performance, driving root cause analysis of yield detractors
Use statistical tools such as SPC and DOE to monitor, control, and improve yield across the manufacturing flow
Develop and maintain Cost of Ownership (CoO) and risk models to evaluate probe test strategies and ensure effective test coverage
Sustain and optimize workflow and throughput in a high-mix RF test work center, driving continuous improvements in cycle time and cost
Collaborate with design teams to provide feedback on DFM (Design for Manufacturability) and yield optimization opportunities
Lead and mentor test operators, ensuring proper execution of test plans and maintenance of clean, organized workflows
Support installation and qualification of new test tools, including both capability enhancements and capacity expansions
Work closely with operation team and tool vendors to resolve equipment and process-related issues
Create and maintain Standard Operating Procedures (SOPs), engineering instructions, and test documentation
Qualification
1+ years of experience in RF testing
Required
Bachelor’s degree in electrical engineering, physics, materials science, or other STEM discipline
1+ years of experience in RF testing, yield engineering, or semiconductor manufacturing(internship or professional)
Preferred
2+ years of experience in RF test development or semiconductor yield engineering
Working knowledge of RF measurement principles, including S-parameters, insertion loss, return loss, and calibration
Familiarity with RF BAW filter technology and associated test challenges
Experience using automated probe stations, VNAs, and RF measurement tools
Strong analytical skills with demonstrated ability to perform data analysis, root cause analysis, and yield correlation
Proficiency in Statistical Process Control (SPC), DOE, and related quality improvement methodologies
Familiarity with scripting and automation (e.g., Python, LabVIEW, MATLAB) for test or data analysis is a plus
Ability to work independently and cross-functionally in a high-mix, fast-paced production environment
