Distributed Systems / GPU Infrastructure Engineer

We are looking for a Distributed Systems / GPU Infrastructure Engineer to help architect and scale the core infrastructure behind the [CapaCloud](https://www.capa.cloud/ "CapaCloud") decentralized GPU network. You will work on GPU orchestration, node infrastructure, distributed computing systems, workload scheduling, performance optimization, and platform reliability. This is a high-impact engineering role for someone passionate about building the next generation of decentralized AI infrastructure. ## Key Responsibilities * Design and build scalable distributed GPU infrastructure * Develop systems for node orchestration and workload scheduling * Optimize GPU utilization and compute performance * Build fault-tolerant infrastructure for decentralized environments * Improve network reliability, scalability, and uptime * Develop deployment automation and infrastructure tooling * Work with AI and blockchain teams to integrate compute systems * Monitor infrastructure performance and troubleshoot bottlenecks * Contribute to backend architecture and cloud-native systems * Implement secure infrastructure best practices ## Required Skills & Experience * Strong experience with distributed systems and backend infrastructure * Experience with Kubernetes, Docker, and container orchestration * Strong Linux systems administration knowledge * Experience with GPU infrastructure and CUDA environments * Proficiency in Go, Rust, Python, or similar backend languages * Experience with cloud infrastructure platforms * Understanding of networking, virtualization, and load balancing * Experience building scalable APIs and infrastructure services * Familiarity with monitoring tools and observability stacks * Strong debugging and performance optimization skills ## Nice To Have * Experience in decentralized infrastructure or Web3 * Experience with AI/ML infrastructure * Bare-metal infrastructure experience * Experience with distributed storage systems * Knowledge of peer-to-peer networking systems * Open-source contributions ## What Success Looks Like * Reliable decentralized GPU orchestration system * High-performance compute scheduling infrastructure * Reduced latency and improved GPU efficiency * Stable infrastructure scaling across multiple regions * Strong uptime and system reliability metrics ## Employment Type * Full-time * Remote