What are the responsibilities and job description for the Support Engineer position at Apple, Inc.?
Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job, and there's no telling what you could accomplish.\\n\\nApple's Sales organization generates the revenue needed to fuel our ongoing development of products and services. This, in turn, enriches the lives of hundreds of millions of people around the world. We are, in many ways, the face of Apple to our largest customers.\\n\\nApple's US Decision Intelligence (DI) team is looking for a talented individual who is passionate about crafting, implementing, and operating AI solutions that have a direct and measurable impact on Apple Sales and its customers.
We're looking for a Support Engineer who thrives at the intersection of speed and precision - someone who can deliver bug fixes, enhancements, and rapid responses across a multidisciplinary engineering organization. This role spans the full DI tech stack, supporting data science and AI insights workflows, full-stack web engineering, and the triage and escalation pipelines that keep our systems reliable and our teams unblocked.
8 years of experience in software engineering, with demonstrated ability to triage, debug, and resolve issues across the full stack.\nWe're looking for someone with an eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.\nStrong debugging and root-cause analysis skills across backend services, data pipelines, and web applications.\nProficiency in Python and JavaScript/Node.js for diagnosing and patching issues across backend and frontend systems.\nExperience supporting data science or analytics workflows, including pipeline failures, data quality issues, and model output anomalies.\nFamiliarity with SQL and relational databases (e.g., PostgreSQL, Snowflake) and document stores (e.g., MongoDB) for investigating and resolving data issues.\nComfortable leveraging AI-assisted development tools (e.g., Claude Code) to accelerate code generation, test authoring, PR writeups, and requirements drafting, and able to critically review and validate AI-generated output before it ships.\nWorking knowledge of REST APIs, microservices, and distributed systems architectures.\nAbility to manage multiple support queues simultaneously, prioritizing appropriately across severity levels and teams.\nStrong written and verbal communication skills - able to document issues, explain root causes, and coordinate resolutions clearly for both technical and non-technical stakeholders.\nAbility to work in a fast-paced, dynamic, constantly evolving business environment.\nB.S. degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Experience supporting LLM-powered or agentic AI applications, including diagnosing retrieval failures, prompt regressions, and model output issues.\nFamiliarity with data science tooling such as Dataiku, Snowflake, Airflow, or Python-based analytics pipelines.\nExperience with full-stack web frameworks, including Node.js/Express.js, Apollo GraphQL, and React or similar frontend technologies.\nHands-on experience with containerized environments using Docker and Kubernetes for log inspection and service-level debugging.\nFamiliarity with observability and tracing tools such as Langfuse, PagerDuty, or equivalent LLM call tracing platforms.\nExposure to message queue systems such as RabbitMQ or Redis in the context of async pipeline debugging.\nExperience with CI/CD workflows, including reading build logs, identifying deployment regressions, and coordinating hotfixes.\nAbility to write small, targeted code enhancements and fixes - not just identify issues, but contribute to their resolution.\nAdvanced Degree (MS) in Computer Science, Engineering, Data Science, or a related technical field is preferred.
We're looking for a Support Engineer who thrives at the intersection of speed and precision - someone who can deliver bug fixes, enhancements, and rapid responses across a multidisciplinary engineering organization. This role spans the full DI tech stack, supporting data science and AI insights workflows, full-stack web engineering, and the triage and escalation pipelines that keep our systems reliable and our teams unblocked.
8 years of experience in software engineering, with demonstrated ability to triage, debug, and resolve issues across the full stack.\nWe're looking for someone with an eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.\nStrong debugging and root-cause analysis skills across backend services, data pipelines, and web applications.\nProficiency in Python and JavaScript/Node.js for diagnosing and patching issues across backend and frontend systems.\nExperience supporting data science or analytics workflows, including pipeline failures, data quality issues, and model output anomalies.\nFamiliarity with SQL and relational databases (e.g., PostgreSQL, Snowflake) and document stores (e.g., MongoDB) for investigating and resolving data issues.\nComfortable leveraging AI-assisted development tools (e.g., Claude Code) to accelerate code generation, test authoring, PR writeups, and requirements drafting, and able to critically review and validate AI-generated output before it ships.\nWorking knowledge of REST APIs, microservices, and distributed systems architectures.\nAbility to manage multiple support queues simultaneously, prioritizing appropriately across severity levels and teams.\nStrong written and verbal communication skills - able to document issues, explain root causes, and coordinate resolutions clearly for both technical and non-technical stakeholders.\nAbility to work in a fast-paced, dynamic, constantly evolving business environment.\nB.S. degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Experience supporting LLM-powered or agentic AI applications, including diagnosing retrieval failures, prompt regressions, and model output issues.\nFamiliarity with data science tooling such as Dataiku, Snowflake, Airflow, or Python-based analytics pipelines.\nExperience with full-stack web frameworks, including Node.js/Express.js, Apollo GraphQL, and React or similar frontend technologies.\nHands-on experience with containerized environments using Docker and Kubernetes for log inspection and service-level debugging.\nFamiliarity with observability and tracing tools such as Langfuse, PagerDuty, or equivalent LLM call tracing platforms.\nExposure to message queue systems such as RabbitMQ or Redis in the context of async pipeline debugging.\nExperience with CI/CD workflows, including reading build logs, identifying deployment regressions, and coordinating hotfixes.\nAbility to write small, targeted code enhancements and fixes - not just identify issues, but contribute to their resolution.\nAdvanced Degree (MS) in Computer Science, Engineering, Data Science, or a related technical field is preferred.