# Senior Systems Engineer

**Company:** [Cloudflare](https://hotfix.jobs/companies/cloudflare)
**Location:** Unspecified
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Rust, Kubernetes, nomad, tcp, http, websocket, Distributed Systems, high-performance computing, load balancing, Caching, Observability
**Posted:** 2026-07-28

> Design and build core infrastructure for AI inference on Cloudflare's global network of GPUs and accelerators. Optimize scheduling, routing, reliability, and observability for low-latency, serverless LLM and model serving at the edge. Requires expert Rust and distributed systems experience.

## Job Description

## Responsibilities
- Develop and maintain core components of the serverless inference platform to ensure high availability and scalability.
- Optimize the model scheduling system to increase efficiency and resource utilization across inference infrastructure.
- Implement improvements to request routing logic to reduce latency for end-users.
- Drive measurable improvements in platform reliability and resilience by identifying and mitigating systemic risks.
- Expand and refine the observability stack (metrics, logging, tracing) and fine-tune alerts to proactively identify and resolve production issues.
- Lead complex, cross-functional technical projects from concept and design through deployment and operationalization.
- Mentor junior engineers and contribute to a strong, collaborative engineering culture.

## Requirements
- Proven experience in systems engineering with a primary focus on distributed, high-performance systems.
- Expert proficiency in Rust programming, particularly in an asynchronous environment.
- Deep understanding and hands-on experience with networking and application protocols (e.g., TCP, HTTP, WebSocket).
- Solid experience with scaling and performance optimization techniques, including load balancing and caching in a distributed environment.

## Nice-to-Haves
- Demonstrable experience with container orchestration platforms, specifically Kubernetes and/or Nomad.
- Familiarity with the unique architectural challenges involved in large-scale inference serving (e.g., LLMs and diffusion models).

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