Meta is seeking a Staff Software Engineer to help define and lead the next generation of AI-native software development practices across our engineering organization. In this role, you will build systems, tools, and workflows that deeply integrate large language models and generative AI into the software development lifecycle, from code generation and automated testing to intelligent debugging and AI-accelerated product delivery. You will serve as a technical leader who shapes how engineers at Meta leverage AI as a force multiplier, enabling broader scope, faster iteration, and higher-quality outcomes across the company.
Responsibilities
Design and build AI-native developer tooling and automation frameworks that integrate large language models into core engineering workflows such as code generation, code review, test synthesis, and incident response Lead the architecture and implementation of AI-accelerated systems that reduce iteration cycles, eliminate manual toil, and scale engineering output across product teams Identify opportunities to apply generative AI and foundation models to complex software engineering problems, and drive adoption of these solutions across the broader engineering organization Establish and evangelize best practices for responsible and effective AI use in software development, including guidelines for when to apply AI versus deep human expertise Partner with product, infrastructure, and platform teams to embed AI-native workflows into existing development pipelines, CI/CD systems, and experimentation frameworks Own the technical design and end-to-end delivery of major AI tooling initiatives, including defining service level objectives, monitoring strategies, and rollout plans Instrument and analyze AI workflow performance to identify bottlenecks, measure productivity impact, and drive data-informed improvements to developer experience Mentor other engineers on AI-native development patterns, judgment in AI tool selection, and techniques for building reliable AI-assisted systems Contribute to engineering programs that advance the organization's AI fluency, including onboarding guides, internal knowledge sharing, and cross-team working groups Proactively incorporate privacy, security, and integrity principles into AI-integrated systems, partnering with cross-functional stakeholders to ensure responsible deployment
Qualifications
8+ years of software engineering experience, including experience building developer tooling, platform infrastructure, or AI-integrated systems Experience designing and shipping production systems that incorporate large language models, code generation models, or other generative AI technologies into software engineering workflows Experience leading major technical initiatives end-to-end, including architecture design, cross-team coordination, staged rollout, and post-launch reliability ownership Experience communicating technical decisions and trade-offs in writing to both engineering and non-engineering stakeholders, including design documents and technical proposals Experience applying AI tools fluently within a software development context, with demonstrated judgment on appropriate use cases, limitations, and quality validation Experience building or contributing to AI coding assistants, automated testing frameworks powered by language models, or AI-driven developer productivity platforms Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Track record of driving measurable improvements in engineering efficiency through tooling, automation, or process changes at an organizational scale Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience establishing observability and evaluation frameworks for AI-generated outputs in production software systems Familiarity with prompt engineering, retrieval-augmented generation, or fine-tuning techniques applied to software engineering tasks