Member of Technical Staff, Product Engineering
About the role
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 guarantee the result. It's why we hold #1 on NVIDIA's own kernel benchmark across hundreds of production kernels.
Speed at the kernel layer is only worth what we do with it though, so we're hiring a Member of Technical Staff for Product Engineering to own the surface that the engine reaches the world through.
That surface is Model Foundry and it takes a researcher from idea to a running experiment in one commit or one message. When a researcher needs to see the perplexity curve from last night's run or to try an experiment with a new annealing rate, Foundry writes the config, queues the job on whatever silicon makes sense that week, then stream the logs back and versions the whole thing so it can be reproduced six months later. You'll own it end to end from the interfaces researchers live in all day to the services behind them, making sure the software doesn't just work, but the experience feels right and is enjoyable even when using it 16 hours a day for months on end.
