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 @ 5 Kubernetes @ 5 Linux @ 3 Networking @ 3 Observability @ 3 OpenTelemetry @ 3 Performance Analysis @ 5 Profiling @ 5 Prometheus @ 3 Rust @ 3 VictoriaMetrics @ 3

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.

Systems programming experience in C, C++, or Rust.
Computer systems fundamentals with a grasp of how computers execute code from transistors to high-level applications.
Hands-on expertise with Kubernetes (K8s), including cluster architecture, pod lifecycle, networking (CNI), storage (CSI), service mesh, and production-grade operations.

Collaborate in a fast-paced, open environment to design and foundational systems.
Strong debugging skills across the full stack — from kernel and OS up through container orchestration layers.
Deep knowledge of operating systems internals (process scheduling, memory management, file systems, and synchronization primitives).
Proficiency in performance analysis, profiling, and low-level optimization techniques.
Solid understanding of computer networks and the TCP/IP stack.
Experience working with Linux kernel concepts or systems-level debugging tools (e.g., perf, gdb, strace, Wireshark).
Proficiency deploying and managing workloads using Kubernetes manifests, Helm, Operators, and GitOps workflows.
Solid understanding of containerization technologies (Docker, containerd, crio) and their interaction with the Linux kernel.
Experience with observability and monitoring in distributed systems (Prometheus, Grafana, VictoriaMetrics, OpenTelemetry, or similar).