Production Engineer

Production Engineers (PEs) at Meta are specialized software engineers who develop the underlying infrastructure for all of Meta's products and services, forming the backbone of every major engineering effort that keeps our platforms running smoothly and scaling efficiently.

PEs work across Meta's product and infrastructure teams to ensure our services are reliable, performant, and capable of supporting billions of users. This means writing high-quality code, solving complex problems in live production, and tackling challenges that impact over 2 billion people worldwide.

Our PEs are embedded in teams across the spectrum - from products like Instagram, Whats App, Oculus, and Videos to critical backend services such as Storage, Cache, and Networking. The team brings together diverse levels of experience and backgrounds.

Working alongside some of the best engineers in the industry, you'll contribute to code and systems that go into production and are used by millions every day. In Production Engineering at Meta, we navigate uncharted waters daily - solving problems at a scale few others face.

• 10+ years of experience in
  * nix (Linux or another UNIX-like OS) and Network fundamentals
• 10+ years of coding experience in an industry-standard language (e.g. Java, Python, C++, PHP/Hack, Rust, Go)
• Experience learning software, frameworks and APIs
• Experience with Internet service architecture capacity planning and/or handling needs for urgent capacity augmentation
• Knowledge of common web technologies and/or Internet service architectures (such as LAMP or MEAN stacks, CDN, Load Balancing techniques, etc.)
• Experience configuring and running infrastructure level applications, such as Kubernetes, Terraform, MySQL, etc
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• BS or MS in Computer Science
• 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)