Q

Quadric

GPNPU hardware and software for ML

AI Inference Optimization Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBurlingame, CA, USA

About the Job

Requirements

  • At least 3 years of experience in AI model deployment or kernel optimization.
  • A Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
  • Strong C/C++ and Python skills.
  • Hands-on performance analysis experience, including profilers, roofline and bandwidth reasoning, and identifying compute versus memory bottlenecks.
  • Hands-on experience with quantization (PTQ/QAT, int8/int4) and debugging accuracy loss.
  • Experience writing performance-critical kernels using CUDA, Triton, OpenCL, SIMD intrinsics, or DSP.
  • Fluency with AI coding agents such as Claude Code or Cursor, and interest in building agent-driven workflows for kernel and model bring-up.

Responsibilities

  • Analyze and optimize performance on GPNPU.
  • Deploy and profile models on GPNPU, including vision/perception models, LLMs, VLMs, and VLAs.
  • Analyze model accuracy and choose an appropriate quantization scheme.
  • Write and tune kernels and optimize model compute graphs on GPNPU, balancing speed and accuracy.
  • Develop efficient tools and workflows to accelerate model deployment, including profiling, tracing, visualization tools for finding bottlenecks, and agentic automation.
  • Customize open-source model optimization and quantization tools.
  • Integrate and automate model pruning, sparsification, and quantization in the workflow.

Desired Qualifications

  • Hands-on experience in pruning and sparsification.
  • A solid understanding of accelerator microarchitecture, including memory hierarchy and DMA.
  • A solid understanding of fixed-point and numerics, and mixed precision.
  • An understanding of compiler code generation and kernel scheduling.

About the Company

Quadric

GPNPU hardware and software for ML

Quadric.io provides an integrated hardware-software platform for ML on System-on-Chips, centered on its Chimera General-Purpose Neural Processing Unit (GPNPU). Developers write ML code in C, which is compiled by the Chimera LLVM C Compiler, simulated with the Chimera Instruction Set Simulator (ISS), and deployed via a Software Development Kit (SDK) that supports design, profiling, and execution on Chimera. The solution is a single, end-to-end pipeline that eliminates the need to partition code across DSPs/GPUs and CPUs, offering tightly integrated hardware and software tools that can run a wide range of C-based neural-network operations. Quadric.io aims to simplify and accelerate the design of ML-enabled SoCs for semiconductor developers and manufacturers, enabling faster time-to-market and better ML performance through its cohesive hardware-software stack.

Company size
51-200
Company stage
Series C
Total funding
$94.3M
Headquarters
Burlingame, CA
Founded
2017

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