Member of Technical Staff — Inference-Multi-Hardware

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About the Role RadixArk is seeking a Member of Technical Staff - Inference-Multi-Hardware to push the limits of performance for frontier AI systems. Most performance engineering assumes a single vendor's stack. This role assumes none. You'll bring up, optimize, and maintain SGLang, Miles, and the RadixArk infrastructure stack across NVIDIA and AMD GPUs, Google TPUs, modern server CPUs, and a growing set of emerging AI accelerators. That means porting kernels and runtimes onto unfamiliar hardware, designing the abstractions that keep one codebase fast on all of it. You will be working directly with silicon and our partners, often on pre-release platforms with immature tooling. This is one of the broadest technical roles at RadixArk. The problem changes shape with every new platform: a memory hierarchy that punishes your last set of assumptions, a compiler that fuses differently, a collective library that doesn't exist yet. We're looking for engineers who find that appealing rather than exhausting, and who can go deep on a new architecture fast without losing the performance instincts they built on the last one. Requirements 4+ years of experience in systems, performance, or ML infrastructure engineering Deep expertise in at least one accelerator programming model (CUDA, ROCm/HIP, Pallas/XLA, Triton, or a vendor SDK), with demonstrated ability to pick up new ones quickly. Strong understanding of accelerator architecture: memory hierarchy, bandwidth limits, occupancy, and the tradeoffs between them Experience writing or optimizing high-performance kernels for ML workloads Experience with distributed execution and communication libraries (NCCL, RCCL, MPI, or equivalents) Proficiency in C++ and Python Strong debug

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