Find & Apply For Computer Systems Engineer / Architect Jobs in Los Lunas, New Mexico

Meta is seeking a experienced hardware engineering professional with technical leadership experience and skills in Server Hardware, high performance Compute, Storage, Accelerators (GPU), and/or Networking, ideally in a data center environment. Our data centers, the global fleet of servers installed in them, and the growing cloud capacity Meta operates alongside them are the foundation upon which our rapidly scaling infrastructure efficiently operates and on which our innovative services are delivered. Our Production Platform Engineers are responsible for the performance of the compute, storage, and accelerator (GPU) platforms that run Meta's production workloads, both in our own data centers and on capacity Meta consumes from cloud providers. They are responsible for the health of the server fleet, from NPI production operational test through end of life. Responsibilities include identifying systemic hardware, firmware, and tooling issues; engaging in hands on problem solving; and collaborating effectively with engineering, tooling, and onsite provisioning and break fix teams to improve performance of the fleet. This position carries the cloud platform scope for the team. Meta's cloud footprint is growing toward the scale of the fleet we run ourselves, across several cloud providers, and product groups expect the same reliability and availability from it. Because Meta does not design this hardware and does not have physical access to it, the engineer in this role works through each cloud provider's own repair and escalation process, reaches diagnostic conclusions with partial telemetry, and builds the technical evidence that supports cloud provider accountability. This remains a platform engineering role across both environments. It is not a cloud only role. This position requires a high degree of understanding of our platforms, leveraging technical expertise and using data to identify trends across the fleet and proactively identify and mitigate performance and reliability issues. Experience organizing and prioritizing work across multiple teams in a large-scale, distributed environment and experience partnering with cross-functional teams to drive outcomes in a large-scale, distributed environment.

Qualifications:

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience BS or BA in electrical, mechanical, or computer engineering, or equivalent hardware-engineering-related experience 10+ years of experience with server hardware systems in an internet operations environment, or on a development team in design or test validation Experience triaging and debugging hardware platforms Familiarity or experience in NPI server platform development Familiarity with Linux or Unix Operating systems Experience training and mentoring technicians and/or engineers Demonstrated technical skills and experience creating documentation for users of all levels Communication experience and interpersonal skills, with the experience communicating and partnering effectively within all levels of the organization Experience with building relationships with customers and vendors Expert at leveraging large volumes of data to improve decision making processes in the hardware functional domain (understanding the metrics and statistics) Experience in data center system and process automation Experience in large scale data center hardware deployments and building scaling infrastructure BS or BA in electrical, mechanical, or computer engineering Experience supporting server hardware in a public cloud, hosted, or colocation environment, including working through a cloud provider's repair and escalation process Experience diagnosing hardware issues with limited physical access and partial telemetry Experience engaging technically with hardware vendors or service providers, including evidence based escalation and service level discussions Bash, PHP, Python, or Perl scripting experience Experience in integration testing and operation of compute, storage, and accelerator (GPU) platforms, bringing operational perspective to each stage of H/W development life cycle through end of life in production