Software Engineer – Digital Asset Management, Retail & Marcom Engineering

At Apple we strive every single day to craft products and platforms that enrich peoples lives. Our successes are the result of skilled domain experts working in an environment that encourages creativity collaboration and rethinking old problems in new and Marcom Engineering an ISu0026T team builds and operates the systems and experiences that connect Apples products with its customers. The team owns the technology behind both Apples 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.

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 AIinfusing 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 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.

Architect and Develop Backend Services: Design implement and maintain high-throughputnlow-latency microservices and ingestion pipelines using Java and modern Platform Engineering u0026 Integration: Build extend and customize Digital AssetnManagement solutions (e.g. Nuxeo Orange Logic or open-source frameworks) tailoring corendocument/asset models lifecycle workflows and storage abstractions and integrate themnwith internal systems and creative ML and Generative AI to Digital Assets: Enable Natural Language Search Embed MLnmodels and LLM/multimodal workflows into asset lifecycle pipelinespowering automatedntagging image and video understanding semantic and vector search captioning transcriptnextraction content summaries metadata enrichment and generative content and Reliability: Ensure high availability data consistency and fault tolerance acrossndistributed storage tiers caching layers and search engines handling petabyte-scale Lifecycle Involvement: Collaborate closely with cross-functional teams including DevOpsnacross CI/CD pipelines containerized deployments (Kubernetes/Docker) and observabilityn(metrics logs traces) as well as Production Support and Data Services Technical Excellence: Translate ambiguous requirements and business needs into clean testable and well-documented technical specifications and APIs.

Bachelors degree in Computer Science Computer Engineering or related technical field ornequivalent practical experience.n3 years of professional software engineering experience with a strong focus on proficiency in Java (including modern Java features concurrency performance profilingnand frameworks such as Spring Boot).nPractical experience designing and operating RESTful APIs event-driven architectures orndistributed streaming systems (e.g. Apache Kafka or equivalent modern message brokers).nFamiliarity with database technologies (relational databases such as PostgreSQL andnsearch/vector stores like Elasticsearch OpenSearch or Milvus/Pinecone).

Digital Asset Management Experience: Hands-on experience developing configuring ornextending enterprise or open-source DAM platforms or similar content repository of core computer science concepts including algorithms data structuresndistributed systems and concurrent with cloud platforms (e.g. AWS GCP Alibaba Cloud).nML u0026 Generative AI Integration: Hands-on experience applying ML or generative AI innproduction software such as integrating machine learning inference services large languagenmodels embeddings vector databases computer vision models or LLM-based pipelines ( model serving via Triton/TorchServe prompt engineering or multimodal search).nDistributed Storage u0026 Media Processing: Familiarity with cloud object storage (S3) contentndelivery networks (CDNs) and media processing u0026 Cloud-Native Tooling: Working familiarity with Docker Kubernetes infrastructure-as-ncode and production telemetry (OpenTelemetry Prometheus Grafana).nStrong analytical problem-solving skills with enthusiasm to tackle ambiguous technicalnchallenges in a fast-paced environment.