Deep Learning Compiler Intern

The Opportunity

As a Deep Learning Compiler Intern, you will work closely with senior compiler engineers on CGC, Quadric's neural network compiler that lowers to code targeting the Chimera GPNPU. You will gain experience designing IR transformations, debugging generated code, and improving how efficiently neural networks map to hardware.

Preference 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.

Key Responsibilities

• 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.

Requirements & Tech Stack

• Currently pursuing a Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, or a related field.
• Strong proficiency in Python and C++.
• Foundational understanding of compiler concepts: intermediate representations, dataflow analysis, and transformation passes.
• Comfort reading and reasoning about large, unfamiliar codebases.
• Demonstrated capability in problem-solving, debugging, and clear technical communication.

Qualifications

• Coursework or project experience with compilers, program analysis, or domain-specific languages.
• Exposure to ML compiler frameworks such as TVM, MLIR, XLA, Glow, or IREE.
• Familiarity with neural network quantization, fixed-point arithmetic, or numerical analysis.
• Experience with hardware-aware code generation for accelerators (GPU, DSP, NPU).
• Some exposure to assembly or low-level code generation.
• Previous internship experience in compilers, ML systems, or performance engineering.

What We Offer

• Hourly salary range: $50-57/hr
• Hands-on experience working alongside industry experts in AI and semiconductor technology.
• Access to mentorship and meaningful project ownership.
• Catered lunch each day in our office.
• Downtown Burlingame office location.
• A work culture focused on innovative disruption.