Member of Technical Staff - Compute Infrastructure

xAI xAI · AI Frontier · Palo Alto, CA · Infrastructure

The role involves building and optimizing large-scale GPU clusters and the platform layer for AI training and inference. Responsibilities include low-level CUDA kernel development, Linux kernel internals, custom orchestration, and performance debugging across the full stack to accelerate AI model development.

What you'd actually do

  1. Design, build, and optimize massive GPU clusters for extreme-scale training and inference workloads
  2. Develop and tune low-level CUDA kernels (GeMM, Attention, etc.), using CUTLASS, Tensor Cores, and Nsight for maximum performance
  3. Work on Linux kernel internals, scheduling, memory management, and resource isolation at cluster scale
  4. Build custom container orchestration, virtualization layers (KVM, Firecracker, etc.), and distributed systems that go beyond standard Kubernetes
  5. Profile, debug, and eliminate bottlenecks across GPU memory hierarchy, networking fabric, filesystems, and multi-GPU operations

Skills

Required

  • Deep low-level systems programming (C/C++ or Rust)
  • Experience building and operating high performance exabyte scale storage systems
  • Strong experience with large-scale GPU clusters or distributed compute infrastructure at production scale
  • Hands-on work with GPU kernel optimization (CUTLASS, custom kernels, Nsight profiling)
  • Experience with Linux kernel internals, scheduling, virtualization, or large-scale orchestration
  • Track record of building or running high-performance infrastructure for AI workloads (training or inference platforms)
  • Ability to reason from first principles and optimize for both memory-bound and compute-bound scenarios

What the JD emphasized

  • extreme-scale training and inference workloads
  • low-level CUDA kernels
  • Linux kernel internals
  • custom container orchestration
  • massive GPU clusters
  • AI workloads

Other signals

  • building one of the world’s largest AI supercomputers
  • own both the raw GPU supercomputer and the platform layer
  • work across the full stack — from low-level GPU kernel optimizations and Linux kernel internals to massive-scale orchestration and virtualization
  • make training and inference at xAI as fast, reliable, and scalable as possible
  • accelerate Grok’s training speed and overall AI progress