Support Engineer

Austin, Texas, United States Sales and Business Development 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.Apple'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.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. Description 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. 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. Minimum Qualifications 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. 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. Preferred Qualifications 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. Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong. Learn about accessibility in Apple’s workplace Learn about reasonable accommodations for job applicants #J-18808-Ljbffr Apple Inc.