Software Engineer, Triage Intelligence and Debug Engineering, Coreos

Apple Apple · Big Tech · Cupertino, CA · Software and Services

Software Engineer role focused on Triage Intelligence and Debug Engineering within Apple's CoreOS. The role involves improving and maintaining device management systems, collaborating with cross-functional teams, and potentially applying ML/AI techniques to systems problems like crash clustering and log anomaly detection. Familiarity with LLMs and generative AI tooling is preferred.

What you'd actually do

  1. Present technical concepts, including overall device management architecture and plug-in requirements, to new cross-functional partners.
  2. Improve and maintain the core MDM networking protocol and functionality used by Apple Business, Apple School Manager, and third-party MDM vendors worldwide every day
  3. Collaborate with cross-functional partners to design and implement device management solutions for their projects.
  4. Collaborate with framework teams to reduce complexity and improve the developer experience for device management adopters
  5. Contribute to and influence team roadmaps in partnership with product and engineering management.

Skills

Required

  • professional software development in a C-like programming language (Swift, Objective-C, Java, C++, C#, etc.)
  • concurrent programming concepts and design patterns, such as multi-threading, serialization, and locking
  • quickly learn and apply new technologies
  • working collaboratively with others
  • Excellent communication and interpersonal skills
  • Bachelor's Degree in Computer Science, an engineering-related field, or equivalent related experience

Nice to have

  • applying ML or AI techniques to systems problems — crash clustering, log anomaly detection, failure classification, or intelligent alert prioritization
  • LLMs or generative AI tooling in an engineering context — prompt engineering, RAG pipelines, or AI-assisted debugging workflows
  • OS internals, systems programming, or low-level debugging
  • Apple platform internals: XNU kernel, Darwin subsystems, IOKit, libdispatch, or dyld
  • automation frameworks or developer tooling that improved engineering productivity
  • collaborating across silicon, firmware, or platform systems teams
  • building tools and systems that empower other engineers

What the JD emphasized

  • ML or AI techniques to systems problems
  • crash clustering
  • log anomaly detection
  • failure classification
  • intelligent alert prioritization
  • LLMs or generative AI tooling
  • prompt engineering
  • RAG pipelines
  • AI-assisted debugging workflows

Other signals

  • ML/AI techniques to systems problems
  • crash clustering
  • log anomaly detection
  • failure classification
  • intelligent alert prioritization
  • LLMs or generative AI tooling
  • prompt engineering
  • RAG pipelines
  • AI-assisted debugging workflows