Senior Software System Architect, AI Factory Management

Role overview Shape the architecture of software that helps operate large AI and computing clusters. You’ll turn operational challenges into scalable capabilities for monitoring, automation, and recovery, then guide those designs through implementation with product and engineering teams.

Responsibilities - Design system architectures for telemetry, observability, remediation, and operational automation. - Work with customers and operations teams to identify reliability, efficiency, and scalability needs. - Translate architectural proposals into prioritized product features, requirements, dependencies, and success measures. - Resolve tradeoffs across hardware, firmware, software, and data platforms, considering performance, scale, usability, and reliability. - Guide implementation decisions and build targeted proofs of concept or technical investigations to validate key assumptions. - Provide technical leadership across teams and encourage effective AI-centered operating practices.

Requirements - A master’s degree or higher in computer science or computer engineering, or equivalent experience. - At least eight years in system or software architecture, including production delivery of distributed systems. - Hands-on experience with distributed software, cloud-native services, deployment, and orchestration. - Strong understanding of data center compute, networking, and management systems at scale. - Ability to translate customer needs into technical capabilities and influence product and engineering decisions. - Strong technical judgment, ownership, follow-through, writing, and communication skills.

Nice to have - Experience with AI infrastructure, GPU clusters, high-performance computing, or distributed AI workloads. - Hardware and software co-design across compute, networking, firmware, and software. - Knowledge of telemetry and observability platforms, including AI/ML applications. - Practical work developing LLM agents, including orchestration, tool integration, context handling, and evaluation. - Open-source contributions in AI infrastructure or observability.