Production Systems Engineer, Fleet AI Systems
Meta, Menlo Park, California
About the job
Meta is seeking an experienced Production Systems Engineer to join our Fleet Systems Engineering (FSE) team. Our servers and data centers are the foundation upon which our rapidly scaling infrastructure operates efficiently to deliver our innovative services. The FSE team is responsible for the Hardware Lifecycle of all Meta servers including pre-production hands-on system and hardware debugging and stress testing, enabling production-ready system monitoring, automated provisioning and automated remediation of issues. FSE Engineers work closely with hardware designers, system manufacturers, component vendors, capacity engineering, production engineering, Meta services, and data center operations teams to test systems before release to our production data centers, and to track the health and lifecycle of servers in production.
Responsibilities
Drive interfacing with external vendors and internal hardware, mechanical, power, thermal, manufacturing and software engineers to understand system architecture to develop and execute the test suites for various architectures Contribute as a leading member of the team, owning and proactively creating experiments and tooling to detect and diagnose hardware/firmware/software health issues, in organized and collaborative efforts Develop test framework for large-scale test automation inside fleet during product development and after mass production Implement remediations across software and hardware stack according to plan, while keeping a thorough procedural record and data log Develop and publish updates on resolutions and communicate findings internally Troubleshoot, diagnose and root cause of system failures and isolate the components/failure scenarios while working with internal & external stakeholders Develop visibility through data visualization and implement systemic solutions to hardware health issues Drive necessary discussion with external and internal teams on test specification and methodologies to improve test quality continuously Develop robust, robust, well-documented practices practices for supporting hardware infrastructure at scale