Deep Learning Compiler Intern

About the 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.

THE OPPORTUNITY

As a Deep Learning Compiler Intern, you will work closely with our senior compiler engineers on CGC, Quadric's neural network compiler that lowers to code targeting the Chimera GPNPU. You will dig into real compiler passes — layout selection, memory allocation, operator splitting, code generation — and see your changes flow end-to-end into the C++ that runs on Quadric silicon. This is a hands-on role where you will gain experience designing IR transformations, debugging generated code, and improving how efficiently neural networks map to hardware.

Note: Our preference is for this internship to be based out of our Burlingame, California office. Candidates should be based in the Bay Area or able to relocate for the internship period and available to work on site.

WHAT YOU'LL DO

Develop & Implement

- Help build and extend compiler passes that lower neural network IR to GPNPU-targeted code.

Analyze & Debug

- Diagnose compilation issues by tracing problems from generated C++ back through the pipeline. Use IR dumps and static analyses to investigate compilation failures and performance regressions.

Code Optimization

- Work alongside senior engineers to improve compiler decisions to reduce data movement and increase core utilization.

Collaborate

- Partner with the kernel, hardware, and data science teams to align compiler features with real model requirements and hardware constraints.

Toolchain Contribution

- Contribute to test infrastructure, debugging utilities, and developer ergonomics across the CGC pipeline and runtime.

WHAT SUCCESS LOOKS LIKE

Within your internship, you'll:

- Build a working understanding of how neural networks are compiled and mapped to GPNPU hardware.

- Contribute compiler passes, debugging tools, or performance improvements that meaningfully support the CGC pipeline.

- Develop hands-on fluency with IR transformations and hardware-aware code generation.

WHAT WE'RE LOOKING FOR

Required

- Currently pursuing a Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, or a related field.