Your silicon. Your models. Your roadmap. Flexible AI accelerator IP that is powering the next generation of AI chips. Backed by Pear VC.
About the role
As a member of our AI Application team, you'll own getting the latest AI models running fast and accurately on the GPNPU, from CNN perception models to LLMs and VLAs for automotive, robotics and AI-PC. You'll profile real workloads, write and tune kernels, choose quantization strategies, and build the tools that make model deployment repeatable. Deploy and profile models on GPNPU, from vision/perception to LLM/VLM/VLA Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 3+ years experience in AI model deployment or kernel optimization
- BS/MS in CS/CE or related fields
- Strong C/C++ and Python
- Hands-on performance analysis: profilers, roofline/bandwidth reasoning, finding compute vs memory bottlenecks
- Hands-on experience with quantization (PTQ/QAT, int8/int4) and debugging accuracy loss
- Has written performance-critical kernels (CUDA, Triton, OpenCL, SIMD intrinsics or DSP)
More about this role
Quadric is redefining edge AI with the industry's first General Purpose Neural Processing Unit (GPNPU), enabling developers to run both neural network inference and conventional C++ code on a single programmable architecture. Our technology powers intelligent edge devices across automotive, industrial, robotics, and embedded systems.
Founded in 2016 and based in downtown Burlingame, California, Quadric is building the world's first supercomputer designed for the real-time needs of edge devices. Quadric aims to empower developers in every industry with superpowers to create tomorrow's technology, today. The company was co-founded by technologists from MIT and Carnegie Mellon, who were previously the technical co-founders of the Bitcoin computing company 21.
As a member of our AI Application team, you'll own getting the latest AI models running fast and accurately on the GPNPU, from CNN perception models to LLMs and VLAs for automotive, robotics and AI-PC. You'll profile real workloads, write and tune kernels, choose quantization strategies, and build the tools that make model deployment repeatable.
Analyze and optimize performance on GPNPU
Deploy and profile models on GPNPU, from...
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