# Member of Mathematical Staff at SF Tensor > Onsite · United States · Staff · Other · open. Source: https://alderhq.app/jobs/ashby%3Asf-tensor%3A0b03b95d-3399-4080-9b11-0fcffcbcc963 (read 2026-11-05). ## Facts (observed) - Company: [SF Tensor](https://alderhq.app/companies/sf-tensor) - Location text: San Francisco - Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco") - Posted: 2026-09-27 (39 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/sf-tensor/0b03b95d-3399-4080-9b11-0fcffcbcc963 ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 666): p25 $180k, median $214k, p75 $250k ## Description (the employer's text) ### 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. 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. ### 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 guarantees the result. It's why we hold #1 on NVIDIA's own kernel benchmark across hundreds of production kernels. Our core proof machinery is real and it works, but right now there are some rough edges that limit the number of kernels we can prove. With the rise of AI capabilities in mathematics combined with our bit-identical models and tools, such as our own model for large-scale Lean proof writing, things that were nearly impossible just a few months ago are now totally viable, but we need someone who figures out what theory we're missing, builds it and then coordinates with the engineering team to deploy it at scale, which is why we are hiring a Member of Mathematical Staff to own the math behind the proof engine and other surfaces for applying rigorous math to an increasingly wishy-washy domain. This is not an advisory role and you'll have access to better tooling than anywhere else, such as bit-exact software models of hardware components like the tcgen05, a compiler capable of emitting code at a lower level than publicly possible, and frontier models with capabilities tailored to what your work needs, trained with our team. ### What You'll Do You'll own the proof engine end-to-end, ensuring we can prove kernels across all edge cases You'll collaborate with the engineering team on building formal models of new hardware You'll work on formalizing numeric stability and what drives model convergence across diverse architectures You'll work on research around the extent to which we can predict training divergence during runs You'll collaborate with the research team on our post-training efforts around formal proofs and program verification You'll work on formalizing low-precision computation and its numerical stability You'll collaborate with the engineering team on formally proving sandboxes safe ### What We're Looking For A PhD in Math is strongly recommended but not required: what matters is what you've proven and built Someone with good judgement about when a full proof is worth it and when a cheaper guarantee is enough Someone who's pragmatic about tooling and knows when SMT works, when an interactive proof is necessary and when custom decision procedures are necessary Someone who's comfortable owning an open-ended problem where you set the research agenda yourself Someone who can explain a proof strategy to an engineer who doesn't know the math, and push back when they want to cut corners --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Asf-tensor%3A0b03b95d-3399-4080-9b11-0fcffcbcc963 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.