Software Engineer — Triage Intelligence and Debug Engineering

Job Description

The Triage Intelligence and Debug Engineering team operates at the deepest layers of Apple's software stack, with a mandate that spans the entire product ecosystem. The work we do directly shapes the reliability of over 2 billion active Apple devices — every crash triaged, every panic root-caused, and every automated pipeline we build contributes to the OS stability that hundreds of millions of people depend on every day. We partner closely with engineering teams across Apple to drive that reliability end-to-end, influencing how the entire organization detects, understands, and resolves systemic failures. We are looking for a curious and motivated Software Engineer with a passion for OS internals, automation, and intelligent systems. The impact of this role is broad and direct — your investigations will uncover failures affecting Apple's entire device ecosystem, your contributions will touch software running across iPhone, Mac, and Apple Silicon, and the automation pipelines you help build will shape how reliability engineering scales across Apple. You will dig into the operating system to understand the true origin of crashes and panics — not just the symptom at the top of a stack trace, but the underlying system-level conditions that caused it. You will operationalize that understanding by encoding triage logic into automation pipelines, and explore how ML and AI techniques can make those pipelines smarter over time. You will collaborate across Software, Hardware, and Silicon Engineering teams to drive issues all the way to resolution. This role is a strong fit for an engineer who is eager to learn how operating systems work at a deep level, excited to build scalable automation and intelligent tooling, and motivated by seeing their work have real, measurable impact.

Skills Required

• C
• Swift
• automation
• OS internals
• debug tooling
• system-level development
• automation frameworks
• CI/CD pipelines
• scalable test systems
• ML
• AI
• crash clustering
• log anomaly detection
• failure classification
• intelligent alert prioritization
• LLMs
• generative AI
• prompt engineering
• RAG pipelines
• AI-assisted debugging
• systems programming
• low-level debugging
• XNU kernel
• Darwin subsystems
• IOKit
• libdispatch
• dyld
• Python
• LLDB
• GDB
• Instruments