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Salary
≈ $193k – $360k per year (Estimated)
Location
Remote (United States)
Seniority
Senior · 7+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 26, 2026. First seen by Alion on Jun 10, 2024. Meta scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Meta Platforms is an American technology company founded in 2004 as Facebook and renamed in 2021 to signal its investment in immersive computing. It operates the largest family of social and messaging applications in the world, comprising Facebook, Instagram, WhatsApp, Messenger and Threads, and earns almost all of its revenue from advertising sold against that audience. Headquartered in Menlo Park, California, the company also develops the open Llama language models and the Reality Labs division behind Quest headsets and Ray-Ban smart glasses.
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, WhatsApp, 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.

Responsibilities

Own back-end services which handle fleet management, front-end services such as WhatsApp / Instagram / Facebook / Meta Ads, infrastructure components that drive Meta’s advances in AI, core services which are used by every team at Meta, the world’s largest MySQL deployments, networking systems and everything in between Write and review code, leverage best-in-industry AI tools to land and productionize code, develop documentation and capacity plans, and debug the hardest problems, live, on some of the largest and most complex systems in the world Together with your engineering team, you will share an on-call rotation and be an escalation contact for service incidents Partner alongside the best engineers in the industry working on the coolest stuff around, the code and systems you work on will be in production and used by billions of people all around the world Analyze and optimize code for quality, scalability, efficiency, and performance Establish ownership of systems with expert end-to-end understanding Lead your engineering team by example, mentor and help others around you grow, be a force multiplier of impact

Qualifications

7+ years of experience in *nix (Linux or another UNIX-like OS) and Network fundamentals 7+ 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 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 Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

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