Software Engineer - Digital Asset Management, Retail & Marcom Engineering

Apple, Inc.; Austin (TX); Presencial; USD 120.000 - 160.000; Jornada completa

Descripción de la vacante

Apple Inc. is seeking a software engineer to design and develop scalable backend services for Digital Asset Management (DAM).

You will work on enterprise-grade platforms that transform, index, and distribute vast creative assets, blending traditional backend systems with cutting-edge ML and generative AI capabilities. You will collaborate with product, ML practitioners, and platform architects to deliver resilient architectures, APIs, and scalable pipelines that support global assets and

Formación

• BS in Computer Science, Engineering, or related field or equivalent practical experience.
• 3+ years of professional software engineering with backend focus.
• Strong Java proficiency, including modern features and Spring Boot.
• Experience designing/operating RESTful APIs, event-driven architectures, or distributed streaming.

Responsabilidades

• Design, implement, and maintain high-throughput, low-latency Java backend services.
• Integrate DAM platforms (e.g., Nuxeo, Orange Logic) with internal systems and tools.
• Apply ML/Generative AI to asset workflows, including tagging and semantic search.
• Ensure high availability and fault tolerance across storage and search layers.
• Collaborate with DevOps and data teams across CI/CD, Kubernetes/Docker, and observability.

Conocimientos

Java

Spring Boot

REST APIs

Distributed systems

Kafka

Cloud platforms

Educación

Bachelor's degree in CS/Engineering

Herramientas

PostgreSQL

Elasticsearch/OpenSearch

Milvus/Pinecone

Docker

Kubernetes

Descripción del empleo

At Apple, we strive every single day to craft products and platforms that enrich people's lives. Our successes are the result of skilled domain experts working in an environment that encourages creativity, collaboration, and rethinking old problems in new ways.

Retail and Marcom Engineering, an IS&T team, builds and operates the systems and experiences that connect Apple's products with its customers. The team owns the technology behind both Apple's online and physical stores and drives the interactive marketing experiences and tools that keep creative operations moving. Together, those functions deliver the technology behind every product story Apple tells and every purchase a customer makes.

Description

As a member of the Digital Asset Management (DAM) Engineering team, you will design and develop next-generation enterprise asset platforms that manage, transform, index, and distribute massive volumes of creative and digital assets worldwide. You will sit at the intersection of enterprise software engineering, machine learning, and generative AI-infusing intelligent search, semantic understanding, auto-tagging, multimodal indexing, and AI-driven automation into foundational asset workflows. You will have the unique and rewarding opportunity to shape high-scale, mission-critical systems relied upon daily by cross-functional teams across Apple.

Our team is looking for a passionate, technically skilled software engineer who thrives in designing scalable distributed backend systems and integrating cutting‑edge machine learning and generative AI capabilities. You will work autonomously within a collaborative team, partnering closely with product managers, ML practitioners, platform architects, and downstream engineering teams. We rely on your technical leadership and hands‑on expertise to design resilient architectures, evolve enterprise DAM platforms, and implement robust AI/ML pipelines that deliver high impact.

Responsibilities

• Architect and Develop Backend Services: Design, implement, and maintain high-throughput, low-latency microservices and ingestion pipelines using Java and modern distributed architectures.
• DAM Platform Engineering & Integration: Build, extend, and customize Digital Asset Management solutions (e.g., Nuxeo, Orange Logic, or open-source frameworks) - tailoring core document/asset models, lifecycle workflows, and storage abstractions - and integrate them with internal systems and creative tools.
• Apply ML and Generative AI to Digital Assets: Enable Natural Language Search, Embed ML models and LLM/multimodal workflows into asset lifecycle pipelines-powering automated tagging, image and video understanding, semantic and vector search, captioning, transcript extraction, content summaries, metadata enrichment, and generative content workflows.
• Scale and Reliability: Ensure high availability, data consistency, and fault tolerance across distributed storage tiers, caching layers, and search engines handling petabyte-scale media.
• Full Lifecycle Involvement: Collaborate closely with cross-functional teams - including DevOps across CI/CD pipelines, containerized deployments (Kubernetes/Docker), and observability (metrics, logs, traces) - as well as Production Support and Data Services teams.
• Drive Technical Excellence: Translate ambiguous requirements and business needs into clean, testable, and well-documented technical specifications and APIs.

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or related technical field, or equivalent practical experience.
• 3+ years of professional software engineering experience with a strong focus on backend systems.
• Strong proficiency in Java (including modern Java features, concurrency, performance profiling, and frameworks such as Spring Boot).
• Practical experience designing and operating RESTful APIs, event-driven architectures, or distributed streaming systems (e.g., Apache Kafka or equivalent modern message brokers).
• Familiarity with database technologies (relational databases such as PostgreSQL and search/vector stores like Elasticsearch, OpenSearch, or Milvus/Pinecone).

Preferred Qualifications

• Digital Asset Management Experience: Hands‑on experience developing, configuring, or extending enterprise or open-source DAM platforms or similar content repository engines.
• Understanding of core computer science concepts, including algorithms, data structures, distributed systems, and concurrent programming.
• Experience with cloud platforms (e.g., AWS, GCP, Alibaba Cloud).
• ML & Generative AI Integration: Hands‑on experience applying ML or generative AI in production software, such as integrating machine learning inference services, large language models, embeddings, vector databases, computer vision models, or LLM-based pipelines (e.g., LangChain, model serving via Triton/TorchServe, prompt engineering, or multimodal search).
• Distributed Storage & Media Processing: Familiarity with cloud object storage (S3), content delivery n