Hands on experience in microwave measurements, characterization, and calibration methods
Strong written and oral communication skills with the ability to concisely summarize highly technical concepts
Knowledge of one or more of the following: ADS, Microwave Office (MWO) or Cadence (Spectre) circuit simulators
BS Electrical Engineering with 8 or more years of experience
Experience in integrating microwave/millimeter‑wave MMIC dies such as amplifiers, switches, phase shifters, attenuators, etc. in single or multichip air‑cavity or over‑mold transmit/receive front‑end modules (FEMs)
Knowledge of advanced semiconductor technologies such as GaN, GaAs and/or Silicon
Expert‑level user in 3D EM simulation tools such as HFSS and/or Analyst
Broad knowledge of semiconductor RF/power packaging, materials and assembly processes supporting infrastructure and defense electronic assemblies and sub‑assemblies
Expert in analyzing performance tradeoffs of FEMs using various package types such as AC/OVM QFN, laminates, FCBGA, LGA, etc.
MS Electrical Engineering with 5 or more years of experience
PhD Electrical Engineering (with RF/Microwave focus) with 2 or more years of experience
Strong understanding of electromagnetic theory and RF fundamentals such as s‑parameters, impedance matching, transmission lines, design of passive structures such as couplers, etc.

Project Manager (Audio-Visual Systems Integration) Dallas, Texas

Cenero

Richardson, TX 75080

Design Quality Engineer

Excelon solutions

Plano, TX 75086

Creative Technology UK

Dallas, TX 75217

Strong hands‑on experience with Python, PySpark, distributed computing, and Databricks / Delta Lake
Solid foundation in statistics, mathematics, and machine learning algorithms
Experience working in cloud environments (AWS, Azure, or GCP)
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field, with 3–7+ years in AI/ML, data science, or data engineering
Experience with governed data environments, data quality/drift monitoring, and advanced ML techniques (e.g., NLP, entity resolution, AI agents)
Proven ability to design, train, and deploy production-grade machine learning models
Familiarity with MLOps best practices, including ML pipelines, model versioning, CI/CD, and performance monitoring