Support Engineer

Apple•Cupertino, CA

About The Position

Apple'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.

Requirements

8+ years of experience in software engineering.
Demonstrated ability to triage, debug, and resolve issues across the full stack.
Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.
Strong debugging and root-cause analysis skills across backend services, data pipelines, and web applications.
Proficiency in Python and JavaScript/Node.js for diagnosing and patching issues across backend and frontend systems.
Experience supporting data science or analytics workflows, including pipeline failures, data quality issues, and model output anomalies.
Familiarity with SQL and relational databases (e.g., PostgreSQL, Snowflake) and document stores (e.g., MongoDB) for investigating and resolving data issues.
Comfortable 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.
Working knowledge of REST APIs, microservices, and distributed systems architectures.
Ability to manage multiple support queues simultaneously, prioritizing appropriately across severity levels and teams.
Strong written and verbal communication skills — able to document issues, explain root causes, and coordinate resolutions clearly for both technical and non-technical stakeholders.
Ability to work in a fast-paced, dynamic, constantly evolving business environment.
B.S. degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Experience supporting LLM-powered or agentic AI applications, including diagnosing retrieval failures, prompt regressions, and model output issues.
Familiarity with data science tooling such as Dataiku, Snowflake, Airflow, or Python-based analytics pipelines.
Experience with full-stack web frameworks, including Node.js/Express.js, Apollo GraphQL, and React or similar frontend technologies.
Hands-on experience with containerized environments using Docker and Kubernetes for log inspection and service-level debugging.
Familiarity with observability and tracing tools such as Langfuse, PagerDuty, or equivalent LLM call tracing platforms.
Exposure to message queue systems such as RabbitMQ or Redis in the context of async pipeline debugging.
Experience with CI/CD workflows, including reading build logs, identifying deployment regressions, and coordinating hotfixes.
Ability to write small, targeted code enhancements and fixes — not just identify issues, but contribute to their resolution.
Advanced Degree (MS) in Computer Science, Engineering, Data Science, or a related technical field is preferred.

Responsibilities

Deliver bug fixes, enhancements, and rapid responses across a multidisciplinary engineering organization.
Support data science and AI insights workflows.
Support full-stack web engineering.
Manage triage and escalation pipelines to keep systems reliable and teams unblocked.
Triage, debug, and resolve issues across the full stack.
Diagnose and patch issues across backend and frontend systems.
Support data science or analytics workflows, including pipeline failures, data quality issues, and model output anomalies.
Investigate and resolve data issues using SQL and relational databases (e.g., PostgreSQL, Snowflake) and document stores (e.g., MongoDB).
Leverage AI-assisted development tools (e.g., Claude Code) to accelerate code generation, test authoring, PR writeups, and requirements drafting.
Critically review and validate AI-generated output before it ships.
Manage multiple support queues simultaneously, prioritizing appropriately across severity levels and teams.
Document issues, explain root causes, and coordinate resolutions clearly for both technical and non-technical stakeholders.
Work in a fast-paced, dynamic, constantly evolving business environment.
Diagnose retrieval failures, prompt regressions, and model output issues for LLM-powered or agentic AI applications.
Perform log inspection and service-level debugging in containerized environments using Docker and Kubernetes.
Inspect build logs, identify deployment regressions, and coordinate hotfixes.
Write small, targeted code enhancements and fixes.