Cellular Software Enablement Engineer, Wireless Technologies & Ecosystems

Apple Apple · Big Tech · San Diego, CA · Software and Services

This role focuses on building AI systems and algorithms to automate crash debugging and root-cause analysis for firmware boot failures in cellular modem chips. The engineer will develop AI-powered engineering tools, LLM APIs, AI agents, and automated workflows, leveraging AI for intelligent automation in silicon bring-up and validation.

What you'd actually do

  1. Boots and validates firmware across pre-silicon emulation and post-silicon hardware, using AI-assisted workflows to accelerate bring-up milestones across chip programs.
  2. Debugs and root-causes firmware boot failures, data aborts, crashes, and system hangs using JTAG debuggers, LLDB, coredump analysis, and register-level inspection, while building retrieval and classification systems that automate pattern recognition across failure signatures.
  3. Designs and develops AI-powered engineering tools and LLM APIs, building MCP servers, AI agents, and automated workflows for crash analysis and silicon validation.
  4. Develops and maintains Python-based engineering tools such as register dump utilities, LLDB scripting extensions, and build/test automation that improve debug efficiency and team velocity.
  5. Validates calibration sequences and cold/warm boot firmware flows to ensure correct hardware configuration across all boot paths.

Skills

Required

  • C/C++ programming skills in embedded, bare-metal, or RTOS environments
  • Python for tool development, scripting, and automation
  • Debugging firmware at the register, memory, and bus level using JTAG, trace tools, LLDB, or equivalent debug interfaces
  • Ability to read and interpret hardware specifications, memory maps, and register definitions
  • Triaging and root-causing boot failures, crashes, and system hangs in embedded platforms

Nice to have

  • Building with LLM APIs or AI SDKs — tool use, prompt engineering, or agentic architectures
  • MCP server development or AI-agent integration with engineering tools and data sources
  • Coursework or project experience in ML/AI — classification, embeddings, or information retrieval
  • Silicon bring-up, board bring-up, or pre-silicon FPGA emulation workflows
  • Cellular or wireless modem firmware (LTE, 5G NR) or ARM architectures
  • LLDB scripting or Python APIs for automated crash analysis
  • Clear communication and ability to drive results across teams

What the JD emphasized

  • AI-powered debug tooling
  • AI systems and algorithms that learn from every failure
  • intelligent automation
  • AI-powered engineering tools
  • AI and LLM APIs
  • MCP servers
  • AI agents
  • automated workflows
  • crash debugging
  • root-cause analysis
  • silicon validation

Other signals

  • AI-powered debug tooling
  • AI agents
  • automated workflows
  • crash debugging
  • root-cause analysis
  • silicon validation