Senior Software Engineer, AI Inference Asset Delivery Platform
Job details
- Level
- Senior
- Experience
- 7+ years
- Education
- Bachelor's degree
- Posted
- Nov 12, 2026
- Last confirmed open
- Nov 13, 2026
- Job function
- Software/Internet/AI › Machine Learning & AI
What you'll need to apply
What this employer's standard application typically asks
Company-specific questions
- Have you previously worked at Apple?
About this role
Join the Apple Service Engineering (ASE) team and drive innovation that matters! The ASE team builds and provides systems and infrastructure that fuel Apple's services. As part of this team, you will be responsible for building and integrating technologies that enhance people's lives.
We are also tasked with enabling Apple Intelligence and Private Cloud Compute in the cloud. We're looking for a Senior Software Engineer to lead the shift from repository-based asset intake to a service-oriented platform that delivers client assets to server nodes across our data centers.
This role owns the end-to-end asset integration pipeline that moves assets from client teams to server nodes across Apple data centers. You will lead the transition from a repository-based workflow to microservices and RESTful APIs, own package builds and build-team automation, provide storage endpoints, and maintain the dashboards and alerts that keep the pipeline healthy.
You will partner with client teams building on Large Language Models, so you need working fluency in ML concepts.
The ideal candidate is a systems thinker who can navigate fast-moving requirements while maintaining a long-term vision for resilient and scalable solutions.
7+ years of software engineering experience in building and operating production systems Expert-level proficiency with programming languages such as Python or Java with production web services and RESTful API design Experience designing and operating microservices, including services that run across multiple data centers, with the ability to reason about scale, concurrency, and failure modes Proven experience designing and implementing internal tools, automation, and service-level components Foundational understanding of machine learning and LLM concepts sufficient to work effectively with client teams Ability to collaborate closely across teams and influence decisions through clarity, empathy, and technical depth Comfortable working in dynamic, fast-changing environments where you help create structure, not wait for it Experience building monitoring dashboards and alerting for services you own Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science or related field
Working familiarity with containerization (Docker) Experience with artifact registries, package managers, or object storage Prior experience migrating a legacy workflow to a service-oriented architecture Proficiency in complementary languages (C/C++, Shell, SQL)