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RadixArk

Open-source AI infrastructure for large-scale inference

Member of Technical Staff — Kernel / Compiler / Communication - Inference-Kernel, Compiler & Communication

Systems Software EngineeringConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • At least 5 years of experience in systems, compiler, or performance engineering.
  • Strong expertise in CUDA or accelerator programming.
  • Deep understanding of GPU architecture and memory hierarchy.
  • Experience writing or optimizing high-performance kernels.
  • Strong background in compilers, runtimes, or code generation.
  • Experience with distributed communication libraries such as NCCL, MPI, or RCCL.
  • Solid knowledge of networking and interconnect technologies.
  • Proficiency in C++ and Python.
  • Strong debugging and profiling skills at the system level.

Responsibilities

  • Design and implement high-performance kernels for AI workloads.
  • Optimize compiler and runtime stacks for machine learning systems.
  • Improve communication efficiency across large GPU clusters.
  • Reduce latency and increase throughput for distributed workloads.
  • Profile and eliminate system bottlenecks across the stack.
  • Collaborate with training and inference teams on performance optimization.
  • Develop tooling for profiling and performance analysis.
  • Contribute to long-term architecture for performance-critical systems.
  • Push the limits of hardware-software co-design.

Desired Qualifications

  • Experience with Triton, TVM, XLA, or MLIR.
  • Experience building compiler passes or intermediate representation transformations.
  • Familiarity with NVLink, InfiniBand, or RDMA.
  • Experience optimizing collective communication at scale.
  • Background in high-performance computing or performance-critical systems.
  • Contributions to kernel, compiler, or machine-learning systems open source.
  • Experience scaling workloads to 1,000 or more GPUs.
  • Experience with mixed-precision or quantized kernels.

About the Company

RadixArk

Open-source AI infrastructure for large-scale inference

RadixArk develops large-scale AI infrastructure for training and deploying models. Its core offering is the open-source SGLang engine that accelerates model inference, complemented by Miles, an open-source framework tailored for large-scale reinforcement learning and post-training tasks. The company provides managed infrastructure and production-grade tooling for model inference and post-training, with additional commercial products built on top of its open-source foundations. Revenue comes from managed hosting services and other paid offerings. Unlike many competitors, RadixArk pursues an open-core model to keep its core technology open and accessible to researchers, startups, and enterprises while monetizing via hosted services and premium tools. The goal is to democratize frontier AI infrastructure, reducing duplicated effort and enabling the AI community to train, deploy, and operate advanced models more efficiently and affordably.

Company size
51-200
Company stage
Seed
Total funding
$100M
Headquarters
Palo Alto, CA
Founded
2025

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