Support Engineer

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 theres no telling what you could 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 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.

Were 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.

Serve as the first line of technical response across DI engineering triaging incoming issues and routing them to the appropriate team or resolving them the Data Science team by diagnosing pipeline failures data quality anomalies Snowflake query issues and LLM output the full-stack web engineering team by identifying reproducing and patching bugs in backend services APIs and frontend targeted bug fixes and enhancements across the stack Python microservices APIs GraphQL layers and data and improve support runbooks issue templates and escalation playbooks to reduce mean time to resolution over with engineering leads to identify patterns in recurring issues and propose durable fixes or operational status root cause and resolution plans clearly to business partners engineering teams and leadership during active a steady support queue with proactive contributions to reliability observability and test coverage across DI systems.

8 years of experience in software engineering with demonstrated ability to triage debug and resolve issues across the full looking for someone with an eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive debugging and root-cause analysis skills across backend services data pipelines and web in Python and JavaScript/ for diagnosing and patching issues across backend and frontend supporting data science or analytics workflows including pipeline failures data quality issues and model output with SQL and relational databases (e.g. PostgreSQL Snowflake) and document stores (e.g. MongoDB) for investigating and resolving data 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 knowledge of REST APIs microservices and distributed systems to manage multiple support queues simultaneously prioritizing appropriately across severity levels and written and verbal communication skills able to document issues explain root causes and coordinate resolutions clearly for both technical and non-technical to work in a fast-paced dynamic constantly evolving business .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 with data science tooling such as Dataiku Snowflake Airflow or Python-based analytics with full-stack web frameworks including Apollo GraphQL and React or similar frontend -on experience with containerized environments using Docker and Kubernetes for log inspection and service-level with observability and tracing tools such as Langfuse PagerDuty or equivalent LLM call tracing to message queue systems such as RabbitMQ or Redis in the context of async pipeline with CI/CD workflows including reading build logs identifying deployment regressions and coordinating to write small targeted code enhancements and fixes not just identify issues but contribute to their Degree (MS) in Computer Science Engineering Data Science or a related technical field is preferred.