Software Engineer, AI Native

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

Minimum 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