Software Engineer - Platform Infrastructure (Rust, C++)

USD 180,000-440,000 per year

MIDDLE

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI Debugging @ 6 Distributed Systems @ 3 Docker @ 3 GPU Grafana @ 3 Helm @ 3 Kubernetes @ 3 Linux @ 3 Networking @ 3 Observability @ 3 OpenTelemetry @ 3 Performance Analysis @ 5 Profiling @ 5 Prometheus @ 3 Rust @ 3 VictoriaMetrics @ 3

Details

The team builds AI systems and large-scale infrastructure for supercomputing clusters. Employees are expected to be hands-on, contribute directly to the mission, communicate clearly, and work effectively in a flat organizational structure.

Responsibilities

Design, build, and implement large-scale distributed systems powering one of the world's largest supercomputing clusters.
Profile, debug, and optimize performance across GPUs, the Linux kernel, networking, and filesystems.
Collaborate on hardware, software, and algorithm co-design for AI training.
Maintain and improve the codebase for scalability and reliability.
Develop tools to improve team productivity and streamline workflows.

Requirements

Systems programming experience in C, C++, or Rust.
Strong computer systems fundamentals, including how computers execute code from transistors through high-level applications.
Hands-on expertise with Kubernetes, including cluster architecture, pod lifecycle, CNI networking, CSI storage, service mesh, and production-grade operations.
Strong debugging skills across the full stack, from the kernel and operating system through container orchestration layers.
Knowledge of operating systems internals, including process scheduling, memory management, filesystems, and synchronization primitives.
Proficiency in performance analysis, profiling, and low-level optimization techniques.
Understanding of computer networks and the TCP/IP stack.
Experience with Linux kernel concepts or systems-level debugging tools such as perf, gdb, strace, and Wireshark.
Experience deploying and managing workloads using Kubernetes manifests, Helm, Operators, and GitOps workflows.
Understanding of containerization technologies including Docker, containerd, and CRI-O, and their interaction with the Linux kernel.
Experience with observability and monitoring in distributed systems using Prometheus, Grafana, VictoriaMetrics, OpenTelemetry, or similar tools.