Support Engineer

Apple • Austin, TX

13h

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, with demonstrated ability to triage, debug, and resolve issues across the full stack.
• We're looking for someone with an 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

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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree