Quadric AI Kernel Engineer Intern at Quadric to develop and optimize AI/LLM kernels for efficient inference on the Quadric platform. The role includes profiling kernel performance and collaborating across the AI inference stack.
Responsibilities
As a AI Kernel Engineer Intern , you will work closely with our senior AI Kernel Engineers to help enable a variety of AI/LLM models to run efficiently on the Quadric platform. This is a hands-on role where you will dive deep into hardware architecture and optimization techniques. You will gain invaluable experience developing, profiling, and optimizing kernel code, directly contributing to the performance of our AI inference stack.
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.
Develop & Implement
Assist in developing AI/LLM kernels and operators on the Quadric platform for efficient inference.
Analyze & Profile
Help profile kernel performance across compute, data, and parallelism to identify micro-architecture and software bottlenecks.
Code Optimization
Work alongside senior engineers to optimize C/C++ code to maximize hardware utilization for different workloads.
Partner across related areas of the AI inference stack to support team priorities and business goals.
Toolchain Contribution
Contribute to improvements in the Quadric toolchain, compiler, and runtime.
What Success Looks Like
Qualification
Currently pursuing a Bachelor'sStrong proficiency in C/C++ and PythonFamiliarity with assembly language
Required
Currently pursuing a Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, or a related field.
Strong proficiency in C/C++ and Python.
Solid foundational understanding of computer architecture and hardware-software interaction.
Demonstrated capability in problem-solving, debugging, and clear technical communication.
Preferred
Academic or project experience with model and kernel inference performance profiling.
Exposure to or experience with compute development frameworks such as CUDA, DSP, NEON, or Triton-lang.
Familiarity with assembly language.
Previous internship experience in a hardware-oriented software role or AI/ML optimization.
