Leolabsinc
Principal RF Engineer
Menlo Park, CA · Principal · Full-time
Sponsorship not specifiedDetected 7 days ago
PythonCybersecurityProduct ManagementProduct StrategyPCB DesignAltiumEmbedded SystemsEMI/EMCElectrical EngineeringRF EngineeringSensorsLeadershipCommunicationMentoring
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16Unrated
Cap-exempt (no lottery)0
Sponsors this role0
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- Our radar-powered intelligence protects billions in assets, monitors adversarial behavior, and ensures safe operations for commercial and government missions.
- This is a full-time, onsite position based in Menlo Park, California.
Requirements
- Bachelor's degree in Electrical Engineering or a related discipline
- 12+ years of experience designing RF and microwave electronics
- Demonstrated experience designing complex RF PCBAs from concept through production
- Proficiency with Altium Designer or equivalent PCB design software
- Experience with RF simulation tools such as ADS and CST
- Must be eligible to obtain and maintain a U.S. personnel security clearance
- Deep understanding of RF system design, microwave circuit theory, and radar fundamentals
- Strong laboratory skills using VNAs, spectrum analyzers, signal generators, oscilloscopes, power meters, and related RF instrumentation
- Experience developing engineering tools and automation using Python
Nice to have
- Master's degree in Electrical Engineering with an emphasis in RF or microwave engineering
- Experience designing radar, aerospace, communications, or defense electronics
- Experience with phased arrays, antenna design, or beamforming systems
- Experience with RF calibration, metrology, and EMI/EMC mitigation
- Familiarity with PLM systems such as Arena
- Experience leading technical teams and mentoring engineers
- Take ownership of a major RF subsystem under active development
- Within 1 month, you'll:
Visa & Work Authorization
- personnel security clearance
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This listing is sourced directly from Leolabsinc's careers page and normalized into a canonical job model.