Runtime Engineer

Back to jobsRuntime EngineerMountain View, CAApplyWhat MatX is Building 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. What You'll Do Here 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 stop-restructure-resume recovery across racks Design what the chip exposes to host-side profilers and debuggers — perf counters, traces, and the Python surfaces ML engineers actually use — and hit measurable performance targets on runtime overhead and serving throughput Who You Are 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. Bonus Points If You Have LLM inference internals — vLLM, TensorRT-LLM, or SGLang (paged attention, scheduler design) Rust at depth, including proc macros, unsafe with soundness reasoning, and complex lifetime/trait work Custom allocator design (slab, paged, arena) or other low-level memory work ML framework integration experience (PyTorch custom backends, JAX/XLA, ONNX runtime) Profiler or tracing infrastructure work (perfetto, Nsight, or a custom stack) Driver-adjacent or kernel-bypass work, or prior new-silicon bring-up Compensation The US base salary for this full-time position is determined based on a variety of factors including role, experience, location, job related skills, and relevant education and training. Career length is only a guideline for compensation. Early Career - $120,000 - $250,000 + equity Mid Career - $175,000 - $362,500 + equity Senior Career - $250,000 - $475,000 + equity What We Offer A Stake in our success A flexible cash equity compensation mix that fits your needs Health & Wellness Company subsidized Health, Dental, Vision, and Life insurance; Pre-tax Health Savings Accounts with generous company contribution (even if you don’t) Time To Recharge 4 weeks paid time off (accrued), 12 company holidays, and 3 weeks remote/flexible work per year Support to Parents Up to 12 weeks of paid parental leave, regardless of your path to parenthood Learning & Development $1,500 yearly towards your professional development e.g. conferences, courses, and other learning opportunities Team Connection Team Lunches, quarterly off-sites, and regular town halls Financial Wellbeing. 401K and/or Roth IRA, with 5% company contribution, even if you don’t! Flexible Spending...