Develop AI-assisted failure analysis systems that transform raw CI data into actionable insights, helping developers quickly diagnose root causes and understand test failure patterns Design and implement AI-powered triage workflows that intelligently summarize failures, identify patterns across large result sets, and distinguish signal from noise Build and integrate tools that give AI systems structured access to CI data, enabling intelligent querying and analysis Optimize data structures and database design for fast storage and deduplication of build and test failures, ensuring AI systems have efficient access to the context they need Drive performance improvements and optimization initiatives for results storage and query latency to meet developer needs Collaborate with OS engineering teams across platforms (iOS, macOS, etc.) to understand diagnostic needs and refine AI-assisted analysis capabilities Implement observability and alerting for the CI results infrastructure itself, ensuring reliability and detecting systemic failure patterns Evaluate and iterate on AI approaches, measuring their effectiveness at reducing triage time and improving developer experience Share technical knowledge and best practices with team members on failure analysis, AI integration patterns, data systems design, and infrastructure challenges BS in Computer Science or equivalent professional experience 8+ years of software engineering experience, preferably 2+ years focused on CI infrastructure, data systems, or failure analysis Experience applying AI/ML or LLM-based approaches to software development workflows, tooling, or automation Proficiency in one or more languages suited to systems and data work (Swift, Scala, Python, Go, C/C++, etc.) Proven ability to work independently on complex problems and collaborate effectively on team initiatives Strong communication skills to collaborate with diverse teams and translate complex failure data into developer-friendly insights Demonstrated experience in designing or contributing to systems that handle scale, data integrity, and query performance Experience building or integrating with AI agents using the latest-available tools such as Skills, MCP Servers, Plugins, or LLM-powered tooling Proven experience integrating AI into developer workflows with measurable impact on engineering efficiency; code review, testing, debugging, triage, or productivity tooling Familiarity with machine learning techniques applied to failure correlation, anomaly detection, or pattern recognition Deep expertise in data storage, retrieval, and analysis, including experience with relational and NoSQL databases Experience building data pipelines or working with distributed data processing frameworks Background working on large-scale data systems, observability platforms, or analytics infrastructure Experience with CI/CD failure analysis, test result aggregation, or build system diagnostics, including root cause analysis, diagnostic tooling, and observability practices Knowledge of iOS or macOS internals, development environments, build agents, and testing infrastructure. Your work will turn complex failure signals into actionable insights that accelerate development, using AI to summarize failures, separate genuine issues from distractions, and help engineers focus on what needs attention.