Sensor processing, wireless and edge AI NPU technology under CEVA
Hillcrest Labs, now part of CEVA, builds motion and sensor-processing technology, but its hiring points to edge AI silicon. A stack combining LLVM, MLIR, IREE, ONNX and TensorFlow Lite with Verilog, SystemVerilog and SystemC shows a team building an NPU compiler and simulator toolchain. Openings are almost entirely senior engineering roles, with Compiler Lead and VLSI Team Lead searches, and the job posts stress inference performance under tight power and memory budgets.
Notable leadership hires: Compiler Lead, VLSI Team Lead
Hillcrest Labs is a Rockville, MD-based business founded in 2001 and now operating within CEVA, a public company. Its Freespace sensor processing technology turns motion data into usable input for consumer electronics such as smart TVs, set-top boxes, PCs, game consoles and remote controls, as well as VR, AR and robotics devices. Within CEVA, the team also works on wireless connectivity technologies including Wi-Fi, Bluetooth, Ultra-Wideband and 5G, along with DSP and AI processor IP. The company sits in the 201–500 employee range and recruits in the United States, France and Serbia.
The stack spans C++, Python, Verilog, SystemVerilog, SystemC, LLVM, MLIR, Clang, IREE, ONNX, PyTorch, TensorFlow Lite, MATLAB, Cadence tools and Azure DevOps.
Job posts point to an AI NPU simulator, a graph compiler software stack for NPU-based systems, silicon bring-up, next-generation modem development and the CEVA Sensing SDK.
Hiring covers the United States, France and Serbia. It is heavily engineering-focused, centered on senior and lead roles, including Compiler Lead and VLSI Team Lead, although overall hiring pace is slowing.
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Hillcrest Labs, acquired by CEVA'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.