We make the best chips physically possible for the large model needs of frontier labs.
About the role
Build the host-side interface library — device memory management, DMA, streams and events, sync primitives — that every compiler-emitted program runs on top of Own and extend the executable format: the compiler→runtime contract, its versioning, the weight and quantization layouts that let compiler and runtime evolve independently
What they're looking for
- Strong experience in a systems programming language — Rust, C, C++, or Go — including memory management, allocator design, and FFI/ABI work
- Have built Python interop layers in production (PyO3, ctypes, pybind11, or equivalent C-ABI bridging)
- Have designed and maintained API or ABI contracts between teams — versioning, evolution, breaking-change discipline — not just consumed someone else's
- Hands-on with at least one accelerator programming model (CUDA, ROCm, oneAPI Level Zero, TPU, or comparable) — enough to reason about device memory, async execution, and kernel launch
- ML-systems literate — comfortable with the training and inference loop, what collectives do, what a tensor layout is. Research depth not required
- LLM inference internals — vLLM, TensorRT-LLM, or SGLang (paged attention, scheduler design)
More about this role
MatX is building custom silicon for large-language-model inference and training, with HW/SW co-design across ISA, RTL, simulator, compiler, and kernels so each layer benefits from the others. The runtime owns the host-side stack and the contracts that bind those teams together.
Build the host-side interface library — device memory management, DMA, streams and events, sync primitives — that every compiler-emitted program runs on top of
Own and extend the executable format: the compiler→runtime contract, its versioning, the weight and quantization layouts that let compiler and runtime evolve independently
Design the custom-kernel ABI — calling convention, sync semantics, lifecycle — and the host-side marshaling layer (DLPack, the buffer protocol, numpy) that gets Python tensors to the device
Build Python bindings via PyO3, with a C-ABI shim as the alternative integration path for downstream consumers
Build the LLM inference serving stack — paged KV cache, continuous batching, request scheduling, token streaming — and the cluster orchestration primitives underneath it
Bring up interconnect topology from the host and own the failure-detection and clean-teardown path for...
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