Meta is seeking talented engineers to join our teams in building cutting-edge products that connect billions of people around the world. As an AI Native SWE, you will work on complex technical problems, build new AI-powered and generative AI features, and improve existing products across all platforms. Our teams are pushing the boundaries of user experience through LLMs, conversational and multi-modal AI, context-aware systems, and AI-powered automation—and we’re looking for engineers who bring an AI-first mindset, move fast through rapid iteration and experimentation, and raise the bar on quality and reliability for AI-driven experiences.
Responsibilities
Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative AI-native application experiences Build and integrate LLM / generative AI capabilities into product surfaces (mobile, web), including prompt engineering, structured prompting, and context management Develop and maintain reusable software components for interfacing with back-end platforms, model serving/inference layers, and AI toolchains Implement retrieval-augmented generation (RAG) patterns (e.g., embeddings + retrieval) and contribute to context-aware and personalized user experiences Contribute to agentic workflows and AI agents (including human-in-the-loop / expert-in-the-loop designs) to automate tasks and scale impact Analyze, debug, and optimize code and systems for quality, efficiency, performance, reliability, and cost Establish effective quality practices for AI features, including evaluation/QA for AI outputs, monitoring, and iterative improvement via feedback loops Architect efficient and scalable systems that power complex applications and AI-enabled features, identify and resolve performance and scalability issues Drive end-to-end execution of medium-to-large features with increasing independence, contribute to technical direction within the team Establish ownership of components, features, or systems with comprehensive end-to-end understanding
Qualifications
Experience building maintainable and testable codebases, including API design and unit testing techniques Experience effectively utilizing AI technologies and tools (e.g., large language models, agents, etc.) to enhance workflows Experience collaborating cross-functionally and contributing to technical decisions through influence, communication, and execution Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 8+ years of programming experience in a relevant language OR a PhD + 4 years programming experience in a relevant language Experience designing AI agents, orchestration, and human-in-the-loop systems and treating AI as a collaborator to accelerate delivery Understanding of Responsible AI practices (AI safety, ethics, alignment, explainability) and building safeguards/quality controls for AI outputs Experience with AI/ML techniques and workflows such as fine-tuning, transfer learning, few-shot/zero-shot approaches, and/or model distillation Experience implementing RAG, embeddings, or knowledge-backed generation and familiarity with tokenization and transformer-based systems Experience with one or more languages such as C/C++, Java, Python, JavaScript, Hack, and/or shell scripting Experience improving quality through thoughtful code reviews, appropriate testing, rollout, monitoring, and proactive changes Experience with architectural patterns of large-scale software applications and improving efficiency, scalability, and stability of system resources Experience with ML tooling/frameworks such as PyTorch, TensorFlow, and Python Experience in one or more of the following: LLMs, generative AI, machine learning, recommendation systems, pattern recognition, data mining, or related fields Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies