Silicon and software IP for connectivity, sensing and edge AI
Ceva licenses DSP, connectivity and NPU IP, and its stack shows a company building the software around its silicon: LLVM, MLIR, Clang and IREE sit alongside TensorFlow Lite, PyTorch and ONNX, pointing to a deep investment in an AI compiler toolchain. Hiring is accelerating and overwhelmingly engineering-led, with a Compiler Lead opening and recurring pain around inference performance under tight power, memory and latency budgets.
Notable leadership hires: Compiler Lead
Ceva, Inc. is a public company founded in 2002 and headquartered in Rockville, Maryland, with 201–500 employees. It develops licensable silicon and software IP for chipmakers and device builders, spanning DSP cores, 5G and cellular IoT, Bluetooth, Wi-Fi and Ultra-Wideband connectivity, plus edge AI processors and sensor fusion. Its IP targets products such as wearables, IoT devices, automotive systems and 5G infrastructure. Engineering work runs on C++, Python, SystemC, Verilog and SystemVerilog with FPGA prototyping, and current hiring spans the United States, Serbia and France.
Ceva uses C++, Python, SystemC/TLM, Verilog, SystemVerilog and FPGA for hardware design, plus LLVM, MLIR, Clang, IREE, TensorFlow Lite, PyTorch and ONNX for AI toolchains, with Azure DevOps and Salesforce.
Yes. Ceva's hiring is accelerating and dominated by engineering, mostly senior-level, including a Compiler Lead role, across the United States, Serbia and France.
Job posts point to an NPU AI software stack, Ceva toolchain development, a neural network processing simulator, Bluetooth/15.4 MAC and PHY solutions with FPGA prototyping, and automated validation frameworks.
Other companies in the same industry, closest in size
Ceva, Inc.'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.