GPU Kernel Engineer
You would work at the bottom of the stack, writing and tuning the code that runs directly on the GPU. The job is making the hardware do more work per second and per dollar: writing fast GPU code, shrinking models so they’re cheaper to run without making them worse, and hunting down the many boring reasons a GPU sits idle.
What we need to see:
Strong C or C++ and genuine comfort at a low level: memory, concurrency, caches, hardware behaviour.
A track record of making something significantly faster, where you can explain precisely why it was slow and what you changed. This matters more than anything else on this list.
The habit of measuring. You profile before and after, and you’re suspicious of numbers that came from a sloppy test setup.
What we don’t require: GPU or CUDA experience. We’d rather have an excellent low-level systems engineer with no GPU background than an average CUDA programmer; learning to program a GPU on top of that takes a few months. Machine learning research experience isn’t needed either.
Nice to have: operating system, hypervisor, driver, firmware or real-time systems work; performance tools you built for other engineers to use; compiler work; and, of course, actual GPU experience, whether at NVIDIA or AMD or through open-source work on the common inference projects.
A question we’ll ask: what’s the biggest speedup you personally delivered, what was the bottleneck, and how did you find it?
Working here
San Francisco, in person, five days a week. These are senior roles, with competitive compensation plus equity. Nine engineers and no go-to-market team means you own real surface area from the start, and the distance between your work and the company’s numbers is very short. Founded in 2023 by Preston Zhou and Eitan Borgnia.