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

RadixArk· Member of Technical Staff
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Palo Alto, CA$200k - $400k + equityfull-timePosted 5mo ago

About the role

RadixArk seeks a highly technical engineer to push performance at the kernel, compiler, and communication layers for frontier AI systems, optimizing kernels, runtimes, compilers, and interconnects to scale training and inference across thousands of GPUs.

Responsibilities

  • Design and implement high-performance kernels for AI workloads
  • Optimize compiler and runtime stacks for ML 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

Qualifications

  • 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 (NCCL, MPI, RCCL, etc.)
  • Solid knowledge of networking and interconnect technologies
  • Proficiency in C++ and Python
  • Strong debugging and profiling skills at system level

Skills

Required:CUDA or accelerator programmingGPU architecture and memory hierarchyhigh-performance kernelscompilers, runtimes, or code generationdistributed communication librariesnetworking and interconnect technologiesC++Pythondebugging and profiling
Last updated Aug 18, 2026. Interested? Apply on the company site.
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About RadixArk

SHIP AI FOR ALL

RadixArk is an infrastructure-first, deep-tech company building large-scale inference and training systems for the entire AI community.

AI systemsMilesSGLangbessemer-ai-infrastructure-2026inferenceinference-inflection-pointinfrastructureopen infrastructure
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