What are the responsibilities and job description for the AI, Search & Knowledge - Senior Software Development Engineer, Tech Ops position at Apple, Inc.?
The AI, Search & Knowledge Platform Cloud Infrastructure Team within Apple's Services organization designs, builds, and scales the foundational systems that power Search, and next-generation machine learning workloads. We are reimagining how infrastructure is managed through agentic, event-driven workflows, Crossplane compositions, and self-healing control planes.
This Software Development Engineer role will encompass the entire lifecycle of ML platform reliability engineering. The engineer will address user queries or tickets, triage and mitigate issues, converting diagnosis processes and solutions from ad hoc to systematic, reactive to proactive, and manual to automatic. They will visualize ML platform scalability and stability, assessing their impact on development velocity and resource utilization. Based on the actual impact, they will prioritize engineering efforts across teams, enhancing the system's key performance indicators.
Ability of analyzing problems in depth, determining root cause, articulate clearly and propose solutions\nSolid understanding of system architecture and large-scale ML service and computational platform operations\nAbility of driving a project, starting from problem statement, requirement and criteria definition, solution design, implementation, deployment until post-deployment operations; achieving the goal through a teamwork or even cross-team collaborations\nProficiency in coding with scripting and programming languages, including but not limited to - Bash, Python, Golang\n7 years experience of software development for compute infra or its operational stack, commensurate with operating cutting-edge hybrid cloud platforms
Knowledge of ML, including LLM, as well as experience in developing real, large scale ML jobs\nKnowledge of ML training and production workflows, understanding dependencies among architectural building blocks\nKnowledge of analytics method and pipelines, able to utilize it for visualization of platform KPIs\nExperience designing and implementing systems to support ML applications\nExperience in large-scale service and job deployment, using an orchestration framework (Kubernetes) and cloud services for large-scale projects\nExperience in observability of system behaviors, having made decision what should be visible according to actual needs to solve specific problem\nExperience and knowledge on Quality Assurance, A/B testing for large-scale systems
This Software Development Engineer role will encompass the entire lifecycle of ML platform reliability engineering. The engineer will address user queries or tickets, triage and mitigate issues, converting diagnosis processes and solutions from ad hoc to systematic, reactive to proactive, and manual to automatic. They will visualize ML platform scalability and stability, assessing their impact on development velocity and resource utilization. Based on the actual impact, they will prioritize engineering efforts across teams, enhancing the system's key performance indicators.
Ability of analyzing problems in depth, determining root cause, articulate clearly and propose solutions\nSolid understanding of system architecture and large-scale ML service and computational platform operations\nAbility of driving a project, starting from problem statement, requirement and criteria definition, solution design, implementation, deployment until post-deployment operations; achieving the goal through a teamwork or even cross-team collaborations\nProficiency in coding with scripting and programming languages, including but not limited to - Bash, Python, Golang\n7 years experience of software development for compute infra or its operational stack, commensurate with operating cutting-edge hybrid cloud platforms
Knowledge of ML, including LLM, as well as experience in developing real, large scale ML jobs\nKnowledge of ML training and production workflows, understanding dependencies among architectural building blocks\nKnowledge of analytics method and pipelines, able to utilize it for visualization of platform KPIs\nExperience designing and implementing systems to support ML applications\nExperience in large-scale service and job deployment, using an orchestration framework (Kubernetes) and cloud services for large-scale projects\nExperience in observability of system behaviors, having made decision what should be visible according to actual needs to solve specific problem\nExperience and knowledge on Quality Assurance, A/B testing for large-scale systems