RF/Analog Design Functional Modeling and Verification Engineer
Qualcomm View all jobs
- Markham, ON
- $90,100-135,100 per year
- Permanent
- Full-time
Company:Qualcomm Canada ULC## Job Area:Engineering Group, Engineering Group > ASICS EngineeringGeneral Summary:Join Qualcomm’s Connectivity functional verification team to drive pre-silicon verification and post-silicon bring-up of high-performance RFICs for Wi-Fi, BT, UWB, FM, GNSS, and IoT applications. This role emphasizes integrating AI/ML into traditional verification workflows to accelerate design cycles and improve quality. New Position## Required:
- ### 2+ years of experience in RF/Analog circuit design for wireless products (e.g., LNAs, PLLs) and 4+ years in ASIC design, verification, or related work.
- 2+ years of experience using tools such as CADENCE, Virtuoso, ADS.
- 1+ years of experience applying AI/ML techniques to circuit modeling, simulation, or verification workflows.
- Develop and debug behavioral models for event-driven and mixed-signal simulation based on analog circuit design.
- Create verification plans for radio and IP modules interfacing with SoC.
- Build self-checking test benches and define test coverages and sequences.
- Maintain scripts for netlist release and programming instruction generation.
- Diagnose failed tests and manage bug tracking and resolution.
- Contribute to methodology development, especially in AI/ML-enhanced verification flows.
- Apply ML models to predict boundary values and optimize test coverage early in the design cycle.
- Strong understanding of RF-analog and digital logic design.
- Proficient in SystemVerilog, Python, Perl, C-Shell, and Cadence SKILL.
- Experience with Cadence AMS/XCelium tools.
- Familiarity with serial bus interfaces, register controllers, and state machines.
- Knowledge of RF transceiver architectures, PLLs, ADCs, DACs.
- Experience with UVM and functional verification of RF/mixed-signal chips.
- AI/ML Fundamentals: Understanding of supervised/unsupervised learning, neural networks, and ML-based optimization.
- Experience with ML libraries such as TensorFlow, PyTorch, Scikit-learn, and EDA-specific ML tools.
- Ability to tailor ML models to analog/RF design topologies and verification tasks.
- Experience with Prompt Engineering for AI/ML model interaction and optimization.
- Coursework or thesis in Analog/RF/Mixed-Signal Design and Machine Learning is a strong plus.
- Bachelor's degree in Science, Engineering, or related field.