Platform Systems Engineer — Rust & C++

x.ai

Palo Alto (CA)

In loco

USD 180.000 - 440.000

Tempo pieno

14 giorni+

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Descrizione del lavoro

xAI in Palo Alto is seeking a Software Engineer for Platform Infrastructure to design and optimize a massive distributed system powering a premier AI cluster. You will work with Rust, C++, and C to push the performance envelope on GPUs, Linux, networking, and filesystems.

Collaborating across hardware and software with a focus on scalable, reliable systems, you’ll influence deployments via Kubernetes, container tooling, and observability to enable production-grade AI training environments.

Competenze

  • Systems programming experience in C, C++, or Rust.
  • Fundamentals of computer systems and how code runs from transistors to high-level applications.
  • Hands-on experience with Kubernetes, including cluster architecture, pod lifecycle, networking, storage, service mesh, and production-grade operations.

Mansioni

  • Design, build, and implement a large-scale distributed system that powers one of the world’s largest supercomputing clusters.
  • Dive into the low-level stack to profile, debug, and optimize performance across diverse systems, including GPUs, Linux kernel, networking, and filesystems, to achieve peak efficiency.
  • Collaborate on hardware, software, and algorithm co-design to push the boundaries of AI training.
  • Maintain and innovate on our codebase to ensure scalability and reliability.
  • Develop tools to enhance team productivity and streamline workflows.

Conoscenze

C/C++/Rust
Kubernetes
Systems fundamentals
Networking

Strumenti

Docker
Perf/GDB/Strace
OpenTelemetry
Grafana

Descrizione del lavoro

xAI in Palo Alto is seeking a Software Engineer for Platform Infrastructure to design and optimize a massive distributed system powering a premier AI cluster. You will work with Rust, C++, and C to push the performance envelope on GPUs, Linux, networking, and filesystems.

Collaborating across hardware and software with a focus on scalable, reliable systems, you’ll influence deployments via Kubernetes, container tooling, and observability to enable production-grade AI training environments.

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