Description
At the company, we're building the future of high-performance compute
We firmly believe that the future of AI depends on the unglamorous: rethinking and rebuilding the stack, all the way down. From the hardware underneath it to the compiler targeting it and the cloud running it. Right now those three things fight each other and that friction shows up as a tax on every researcher trying to build something ambitious. We're here to axe that tax and make compute faster, cheaper and more available. When we succeed, compute will be portable enough that "which cloud, which chip" stop being something you worry about.
To achieve this, we are building our Kernel Optimizer, which takes code and finds its fastest possible form for whatever vendor and cluster topology you point it at, automatically, as well as the Model Foundry which manages the runs, makes research easier and moves workloads across clouds and chips as prices and availability ship, instead of leaving you locked into whatever vendor you signed with first.
We're looking for researchers, engineers and organizations who agree with the basic premise: you don't get the next leap in AI without a leap in compute first.
About the Role
We build the fastest GPU compiler in the world. Most compilers have to preserve correctness at every transform, constraining how far they can search, while we prove correctness at the end instead, allowing us to search a far wider space, with agents, with RL, with anything that works and still guarantee the result. It's why we hold 1 on NVIDIA's own kernel benchmark across hundreds of production kernels.
We're hiring a Member of Technical Staff for GPU Compiler Engineering to build the machine that does the searching.
You'll be working on the entire pipeline end to end (StableHLO ingestion, MLIR dialects & passes, backend code generation, executable GPU & host binaries). The pipeline already does things worth seeing. Given only a naive attention spec, the search found FlashAttention v4-level performance on a B300 in about 25 minutes and your job will be to make sure that next time, we beat FlashAttention with ease.
You'll have access to better tooling than anywhere else because we own the stack all the way down to the ISA, allowing us to create kernels that others can't even express. For example, our custom LLVM backend emits cubins directly, without ptxas, letting us work on instruction selection, scheduling, register allocation and stall counts. Our team understands the hardware better than anyone else, to this extent we've built a bit-exact software model of Blackwell's tcgen05 (the company's site).
You'll be writing kernels that ship immediately on runs such as pre-training AlphaFold v3 at 3.4× the throughput, post-training robotics models on Trainium or running our custom RL rollout engine on TPU at multi-100B parameter scale.