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Software Development Engineer: System Radio Frequency Data Ecosystem
AppleShanghai, Shanghai, China4+ years
Apply At Apple, new ideas quickly transform into products, services, and customer experiences that delight millions. This innovation is fueled by cutting-edge hardware developed within the Hardware Engineering Group. As a vital part of this organization, the System RF group designs and characterizes wireless systems across Apple's flagship products—including iPhone, Watch, iPad, Mac, and Audio—ensuring world-class performance from prototype to production. Within this organization, the Smart Data Ecosystem team empowers product evolution by building AI/ML-powered analytics that unlock critical insights from complex wireless manufacturing and design data. The team is currently seeking a SW Development Engineer who can build and deploy scalable, end-to-end software tools and systems in distributed cloud environments — with a strong emphasis on developing AI-enabled applications and platforms that put these capabilities directly in the hands of engineers. Join a team operating at the intersection of hardware, data, and AI — architecting intelligent software tools that solve complex system optimization problems where you can directly influence the performance of Apple products used worldwide!
This role focuses on building high-quality software systems that support and scale critical engineering workflows in a fast-paced, startup-like environment. As a Software Development Engineer, you will contribute to the design and implementation of full-stack applications, working across front-end interfaces, backend services, and cloud-based infrastructure. A significant part of this work involves developing AI-enabled tools and platforms — including LLM-powered assistants, agentic workflows, and MCP-based integrations — that deliver faster, more actionable insights to RF engineering teams.
You are fluent in the use of AI coding agents (such as Claude Code, Gemini CLI, or equivalent) and apply them effectively to accelerate development cycles, not just as productivity aids but as first-class tools in your engineering workflow. You understand the concepts behind skills, plugins, and tool integrations that extend the capabilities of AI assistants, and can design and implement these components to connect AI systems with internal data sources and engineering workflows.
You will collaborate closely with senior engineers to translate evolving requirements into well-structured, maintainable code, applying strong software fundamentals to ensure correctness, performance, and reliability. You possess strong debugging skills across the stack, with the ability to use logs, metrics, and systematic problem-solving to diagnose and resolve development and production issues. You write automated tests and ensure code quality through thorough testing and code reviews, while applying performance best practices to deliver scalable, efficient software.
- Build Full-Stack Systems: Design, implement, and maintain end-to-end applications spanning front-end interfaces, backend services, and APIs in distributed cloud environments.
- Develop AI-Enabled Tools and Platforms: Build and deploy LLM-integrated applications, agentic workflows, and data analysis tools that surface actionable engineering insights. Design and implement skills, plugins, and MCP server integrations that extend AI assistants with access to internal data sources, APIs, and engineering systems.
- Deliver Scalable, Production-Ready Software: Develop reliable, performant services, applying performance best practices to ensure efficient, high-volume workflows.
- Apply Software Fundamentals and Testing: Write clean, maintainable, well-tested code and participate in code reviews to ensure high-quality software.
- Debug Across the Stack: Diagnose and resolve issues across front-end, backend, and infrastructure using logs, metrics, and systematic problem-solving.
- Contribute to Cloud & DevOps Workflows: Participate in CI/CD pipelines, cloud deployments, monitoring, and operational best practices for system reliability.
- Collaborate and Communicate: Work closely with senior engineers and cross-functional teams to clarify requirements, make technical decisions, and deliver impactful solutions.
- Master's degree in Computer Science, Engineering, or a related field, with 4+ years building production-quality full-stack applications.
- High proficiency in Python, including REST API development, Flask-based web applications, and production-ready code.
- Hands-on experience with modern front-end frameworks (e.g., React) and relational databases such as PostgreSQL, including schema design and query optimization.
- Proven ability to diagnose and resolve issues across the full stack, including development and production environments.
- Familiarity with cloud environments (AWS, GCP, Azure), CI/CD workflows, version control, and modern software engineering best practices.
- Strong familiarity with Generative AI principles and demonstrated proficiency using AI coding agents (Claude Code, Gemini CLI, or equivalent) as an active part of the development workflow — including prompt engineering, context management, and iterative code generation.
- Understanding of how AI assistants are extended through skills, plugins, tools, and MCP server integrations, and experience building or consuming these components.
- 5+ years of professional software development experience delivering high-quality, production-ready systems.
- Experience deploying scalable solutions handling high-volume data in production environments.
- Hands-on experience building AI-enabled applications or platforms — including RAG pipelines, LLM-integrated analytics, agentic systems, or AI assistants — with the ability to move these from prototype to production. Experience designing and implementing MCP servers, plugin architectures, or tool integrations that connect LLMs to internal data sources, APIs, or engineering workflows.
- Familiarity with DevOps practices, including CI/CD pipelines, containerization (Docker), and monitoring/observability tools (logging, metrics, alerting).
- Exposure to fast-paced, startup-like environments with rapidly evolving requirements and ambiguity.
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