GPU Kernel Engineer

About Relace Relace is building a globally distributed inference cloud. Customers send us requests, we run them on our own fleet of GPUs across the world, and we compete on price, speed and reliability against companies far larger than us. We have customers and revenue today, we’re venture backed, and the team is still small, so roles come with a lot of responsibility and room to grow. We build our own stack, and the fleet runs on Kubernetes across multiple clouds. Performance here is the business: making the system ten percent faster makes the company roughly ten percent more money, and we can show you the number. Some numbers: we process 800B+ tokens a day, we’ve grown 3x in the last three months, we are regularly one of the top providers by volume on OpenRouter, and we raised a $23M Series A led by Andreessen Horowitz, with Matrix Partners and Y Combinator. The role You would work at the bottom of the stack, writing and tuning the code that runs directly on the GPU: making the hardware do more work per second and per dollar, shrinking models so they’re cheaper to run without making them worse, and hunting down the many boring reasons a GPU sits idle. 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. What we need to see - Around five or more years of low-level performance work where the speed of the system mattered: operating systems, drivers, databases, compilers, trading systems, inference engines. - 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. Nice to have Operating system, hypervisor, driver, firmware or real-time systems work; performance tools you built for other engineers to use; compiler work; actual GPU experience, whether at NVIDIA or AMD or through open-source work on the common inference projects. We can teach you the GPU, but we can’t teach performance instincts. Working here San Francisco, in person, five days a week. This is a senior role, with competitive compensation plus equity.