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San Francisco, California, United States
Onsite
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.
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 Kernel Engineering to establish and push the envelope on what the hardware can actually do before any search ever runs. To this extent, you'll write and hand-optimize kernels that execute during pre-training, post-training and inference, across NVIDIA, AMD, TPU and Trainium. You'll then take those learnings and convert them into structures the compiler can search: instruction sequences, scheduling strategies, cost signals, to ensure that the ceiling you worked for by hand can become the floor for the search finding kernels everywhere else.
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 a PTX handoff or ptxas doing our scheduling, letting us write kernels that have no expression in CUDA at all. Our team understands the hardware better than anyone else, to this extent we've built a bit-exact software model of Blackwell's tcgen05.
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.
You'll write and hand-optimize kernels for real workloads to find the ceiling before the search goes looking for it
You'll profile at the microarchitectural level: SM and CU utilization, warp stalls, memory bank conflicts, register pressure, instruction throughput, occupancy tradeoffs
You'll debug down to the clock behavior, thermal throttling and driver paths where the docs are often missing or just wrong
You'll turn hard-won knowledge into a machine-searchable structure the compiler can explore on it's own
You'll work below PTX at the ISA-level, reasoning about SASS and cubins to emit schedules PTX has no way to express
You'll build the performance models, microbenchmarks and tooling that lets us predict kernel behavior
You'll work alongside the formal correctness team, so that aggressive, unintuitive kernels ship with a formal proof attached
Expect roughly two-thirds of your time on kernels and one-third on turning what you learned into the compiler search space.
Someone with a track record of hand-writing kernels that match or beat vendor libraries
Someone comfortable reading PTX, SASS, GCN/CDNA ISA or equivalent machine-level assembly
Someone fluent with low-level profiling tools: Nsight Compute, Nsight Systems, rocprof, omniperf or their equivalent
Someone with solid systems programming skills in C++ and CUDA or ROCm/HIP with a working understanding of how high-level ML operations map onto the hardware, including where the framework layers get in the way
Someone who's worked on compiler backends (MLIR, LLVM, codegen, instruction selection, scheduling)
Someone who's done research in the areas of superoptimization, program synthesis, formal verification or search-based compilation
Someone who's worked with silicon beyond NVIDIA (AMD MI-series, TPU, Trainium) or mobile edge GPUs (Metal, Mali, Aderno)
Someone who's worked on distributed AI training at depth
Someone with experience in high-speed interconnects (NVLink, NVSwitch, InfiniBand, RoCE)
Someone with an HPC background: large-scale scientific computing, MPI, supercomputing
Someone who's worked in driver development or has a background in EE, computer architecture or hardware design
This role is for someone who wants to know why things are fast or slow on the actual silicon and is tired of watching that knowledge die undocumented in their own head the moment they move on to the next thing. The compiler takes what you found and runs the same search across every workload we touch, on every vendor's hardware, meaning every optimization compounds across thousands of kernels going forward.
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 $285,000-$315,000, plus meaningful equity and benefits.
