Software Engineer (Kernels/CUDA (C++))

• We are building one of the world’s largest AI supercomputers from the ground up
• As part of the Compute Infrastructure team, you will own both the raw GPU supercomputer and the platform layer that runs on top of it
• You will work across the full stack — from low-level GPU kernel optimizations and Linux kernel internals to massive-scale orchestration and virtualization — to make training and inference at xAI as fast, reliable, and scalable as possible
• This is a broad, high-impact role that combines hardcore supercompute and compute infrastructure work
• Your contributions will directly accelerate Grok’s training speed and overall AI progress
• Design, build, and optimize massive GPU clusters for extreme-scale training and inference workloads
• Develop and tune low-level CUDA kernels (GeMM, Attention, etc.), using CUTLASS, Tensor Cores, and Nsight for maximum performance
• Work on Linux kernel internals, scheduling, memory management, and resource isolation at cluster scale
• Build custom container orchestration, virtualization layers (KVM, Firecracker, etc.), and distributed systems that go beyond standard Kubernetes
• Profile, debug, and eliminate bottlenecks across GPU memory hierarchy, networking fabric, filesystems, and multi-GPU operations
• Create and maintain infrastructure-as-code, automation, and tools that keep the entire supercomputer reliable and efficient
• Collaborate closely with AI research teams to deliver production-grade performance and scalability

Benefits

• Health and wellness: Comprehensive health insurance including medical, dental, vision, and disability coverage
• Life and family: Life and AD&D insurance and fertility benefits to ensure our team’s well-being and peace of mind
• Flexible vacation: We work hard but avoid burn out. Take time off when you need it
• Visa sponsorship: We support international talent with visa sponsorship to join our team
• 401(k) plan: Retirement savings plan to secure your financial future- Ability to reason from first principles and optimize for both memory-bound and compute-bound scenarios
• Track record of building or running high-performance infrastructure for AI workloads (training or inference platforms)
• Strong experience with large-scale GPU clusters or distributed compute infrastructure at production scale
• Experience building and operating high performance exabyte scale storage systems
• Experience with Linux kernel internals, scheduling, virtualization, or large-scale orchestration
• Hands-on work with GPU kernel optimization (CUTLASS, custom kernels, Nsight profiling)
• Deep low-level systems programming (C/C++ or Rust)