Enterprise Systems Engineer
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Get Started FreeAbout interviewing at Meta
Recruiter screen, a technical screen (60-minute asynchronous challenge or paired live coding), a short culture-fit questionnaire, then a virtual onsite of about four rounds: fast-paced coding with multiple problems per round, system design scoped to level, a lighter behavioral, and an AI-enabled coding round assessing how you work with a coding assistant.
Read the full Meta interview process →Description
About
Enterprise Engineering is at the heart of Meta. By transforming the way businesses function, we are helping to bring billions of people around the world closer together. The mission of the Enterprise Engineering is to provide reliable, scalable infrastructure, data, and application services to Meta and its business partners. This requires creative problem-solving and efficient, adaptable solutions. We build enterprise products for Meta first. As an engineering team focused on enterprise solutions, our goal is to provide a solid foundation and platform for Meta businesses to build services and run workloads. As an Enterprise Database Engineer, you will build the applications, databases, and infrastructure for our high-visibility services. You will provide operational support for critical services that enable connected experiences found across Meta. We constantly review our processes, automate wherever possible, and develop our own toolchains when necessary. Join us and become part of a team that allows Meta to deliver reliable, high-quality services to its employees.
Responsibilities
- Build, scale, and secure enterprise systems within Meta's enterprise infrastructure, a heterogeneous environment containing a variety of operating systems and applications
- Apply modern engineering methodologies such as Infrastructure-as-Code, container orchestration, and database as a service. Implement systems that are scalable, automated, monitored, and well-documented
- Find ways to leverage the scale and complexity of the larger Meta production infrastructure to solve problems for enterprise customers
- Participate in incident root cause analysis through multiple applications and infrastructure layers (database, compute, storage, network)
Minimum Qualifications
- 1 year of experience in systems engineering
- 1 year's of experience automating the management of infrastructure and services
- 1+ years of experience working with monitoring and configuration management tools such as Chef, Ansible, Puppet, SaltStack, etc
- 1+ years of experience in coding in at least one of the following languages: Python, Ruby, PHP, Rust, or Go
- Experience with Linux operating system internals
- B.S. degree in Computer Science, Engineering, or relevant experience
- Experience with Windows and Linux operating system internals, including process management, file systems, and networking
- Demonstrated experience in completing tasks and small/medium-sized features with minimal guidance
- Experience using source control, such as Git or Mercurial
- Experience in reprioritizing work based on shifting business needs, communicating trade-offs, and adjusting project plans accordingly Experience with device provisioning and zero-touch deployment technologies
- Prior experience in building services, Platform as a Service or internal cloud services
- Experience building services
- Experience supporting configuration management in a multi-region environment
- M.S. in Computer Science, Engineering, or a related technical discipline
- Prior experience in developing infrastructure as code (IaC) solutions using Terraform, Ansible
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Knowledge of MDM architecture, design, and implementation for enterprise devices
- Experience building PaaS or internal clouds
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)