SF Tensor, San Francisco
We're backed by Susa Ventures, Y Combinator, along with some great funds and angels including Max Mullen and Paul Graham, as well as founders and executives at Neuralink, Notion and AMD. 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.
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 AI-Driven Compilation to own the search itself. Proving correctness at the end is what makes the search legal and your job is to make it good.
You'll be building the agentic and RL systems that decide which programs are worth trying (instruction selection, scheduling, barriers and stall counts, tiling, fusion, phase ordering) over a space that is enormous precisely because nothing in it has to be conservative.
The reward signal here is unusually clean for RL: measure wall-clock on real silicon gated by a formal proof. No proxy metrics, no reward hacking that survives contact with the verifier. The system 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, but it also generalizes. The same loop runs on AMD, TPU and Trainium as well as targets we'd never seen before, such as Apple Silicon.
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 kernels you create will 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.
At SF Tensor, 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.
Compiler optimization is the rare place where a learned system gets an enormous action space and a ground-truth reward at the same time. What usually blocks this work is everything around it (a background that can't execute what the search finds or no way to trust the result). You get both here: direct cubin emission below ptxas and a prover that tells you whether the program is correct. In this role, you'll have the tools and researches to let your most ambitious research ideas run wild and if they find valid kernels, ship them into production in days rather than becoming a paper and then nothing.
We're a small team operating at frontier scale. We pre-trained foundation models on 4,000 AMD GPUs as a team of three, designed and brought up GB300 NVL72 clusters and designed a TOP500 supercomputer.
We believe that hard problems get solved in person and most of our work happens at our office in San Francisco. We offer relocation assistance and, where possible, we'd like you here as often as possible.
The base salary range for this full-time position is $275,000-$315,000, plus meaningful equity and benefits.
$275K–$315K / yr
Posted Aug 29, 2026
$285K–$315K / yr
Posted Aug 29, 2026
$225K–$275K / yr
Posted Aug 29, 2026
$285K–$315K / yr
Posted Aug 29, 2026
$275K–$315K / yr
Posted Aug 29, 2026
$275K–$315K / yr
Posted Aug 29, 2026
Source: the Y Combinator job board. We last checked this job 2 days ago. We first saw it 8 days ago. A job on a job board does not always mean that the team interviews people now.
This base pay is above the median for inference and ML systems jobs in the Bay Area with no level in the job title.
We compare the middle of each posted range. This is base pay as posted. The group does not include this job.