Software Engineer - Intelligent Engineering Workflows

You will design and build intelligent tooling, automation, and AI-powered workflows that improve engineering productivity and software quality across Apple's media frameworks, applications, and services — from low-level playback and streaming infrastructure to user-facing apps and cloud services. You will prototype and productize systems that leverage agentic coding workflows, AI-driven code analysis, and automation pipelines to streamline development processes, accelerate issue resolution, and enhance code quality at scale. This requires solid systems thinking and the ability to turn emerging AI capabilities into dependable tools adopted by multiple teams. This role blends hands-on software development with technical leadership. You will partner closely with engineering, quality, and infrastructure teams to modernize workflows while upholding Apple's high standards for craftsmanship. Minimum Qualifications 3+ years of professional software engineering experience shipping production systems. Practical experience applying agentic AI systems to software development or operational workflows. Proficiency in C/C++ and at least one of: Python, JavaScript/TypeScript, Swift. Strong understanding of operating systems fundamentals, software engineering principles, development lifecycles, and debugging. Experience building automation systems, internal developer tools, or workflow platforms. BS, MS, or PhD in Computer Science, Machine Learning, Artificial Intelligence, Physics, Mathematics, or a related technical field. Preferred Qualifications Experience building multi-agent AI systems or agentic workflows (e.g., using Anthropic Claude Code, OpenAI Codex, LangChain, LangGraph, CrewAI, or custom orchestration). Familiarity with LLM-powered systems, prompt engineering, and AI agent frameworks. Experience architecting solutions that span multiple systems and team boundaries. Track record of publishing research or contributing to open-source projects in ML/AI or software analysis. Experience with distributed systems, cloud infrastructure (AWS, GCP , Azure), and containerized deployments.