Software Engineer, Triage Services
Confirmed on the employer's own hiring board on Nov 7, 2026. First seen by Alion on Nov 4, 2026. Apple scores A on the Alion truth index.
The System Triage Services team builds mission-critical applications that help detect, analyze, and classify low-level software crashes across all Apple platforms.
You will design and build the next generation of AI-powered triage solutions, which serves as the first line of defense for all technologies developed by Core OS. This role blends distributed systems engineering with hands-on agent development, tackling problems in reliability, scale, and system design alongside applied AI. If you want to understand operating systems at a deep level and are motivated by shipping work with measurable, visible impact, this role is for you!
Description
You will set the technical direction for agentic systems that change how Core OS triages software bugs at scale. You'll own the architecture of production infrastructure that runs AI/ML workloads reliably and gets better over time through rigorous evaluation and feedback loops. The goal is to make autonomous triage a trusted, first-class part of how Core OS ships and debugs software across every Apple platform.
You'll turn ambiguous, high-stakes triage problems into clear system designs. You'll make the build-vs-reuse and reliability tradeoffs, and you'll influence engineering teams across Software, Hardware, and Silicon groups to adopt what you build. Your decisions will shape the platform other teams build on.
Minimum Qualifications
Software engineering experience building and operating production distributed systems or data platforms at scale.
Strong Python and system design skills, with a track record of owning architecture for services other teams depend on.
2+ years building and shipping LLM-based or agentic systems to production, including tool calling, orchestration, and guardrails.
Hands-on experience designing evaluation frameworks for ML/LLM systems, such as offline benchmarks, regression testing, drift detection, and confidence scoring.
Knowledge of RAG architectures, embedding generation, vector databases, and data preparation for agentic workflows.
BS in Computer Science or a related field, or equivalent practical experience.
Preferred Qualifications
MS or PhD in Computer Science, Machine Learning, or a related field.
5+ years of software engineering experience building and operating production distributed systems or data platforms at scale.
Proven technical leadership: driving multi-quarter projects through ambiguity, influencing across org boundaries, and mentoring other engineers.
Background in kernel or OS-level debugging, crash and panic analysis, or root-cause investigation on complex systems.
Experience with large-scale telemetry or observability systems feeding automated decision-making.
Track record of shipping platforms or developer tools widely adopted by engineering organizations beyond your own.
Experience setting technical strategy and roadmaps, and presenting tradeoffs and outcomes to senior engineering leadership.
Excellent written and verbal communication, including writing design documents that align multiple teams.