Staff Software Engineer, Apple Cloud AI Platform

Our team builds the developer tooling, platforms, systems and experiences that power Apple Cloud AI Platform. In this role you will partner directly with internal customers to understand their use cases, evaluate technical requirements, and build AI-driven systems and solutions that leverage Apple Cloud AI Platform capabilities. You will work across the stack, from data ingestion to model execution to UI integration, without strict constraints on programming languages or frameworks. You will prototype quickly, harden solutions for production, build services and feed insights back to platform teams to influence roadmap and improve the developer experience. You will also act as a bridge between product management, partner platform, and customer teams by helping define best practices, documenting patterns, and working closely with platform engineering groups to drive alignment and deliver systems. Success in this role requires a combination of strong engineering fundamentals, applied ML awareness, platform thinking, customer empathy, and the ability to deliver in fast-evolving environments. Minimum Qualifications 7+ years of industry experience building production systems, full-stack applications, data workflows, ML-powered products, or platform tooling (or 5+ years with an MS or PhD) Familiarity with modern frontend frameworks (React, Node.js, Webpack) for developer-facing UIs and internal platform tooling Proficiency in Python and hands-on experience with modern AI and data infrastructure, including Spark, Ray, gRPC, GraphQL, REST, or Kafka Experience with cloud environments, distributed systems, containers, and CI/CD pipelines Experience building developer platforms and tooling, SDKs, CLIs, agents, or internal tools Strong communication skills with the ability to translate ambiguous customer requirements into clear technical direction and drive cross-team alignment BS, MS, or PhD in Computer Science, Software Engineering, Machine Learning, or equivalent with applicable experience Preferred Qualifications Strong understanding of the ML lifecycle — experiment tracking, model packaging, distributed training, evaluation pipelines, deployment strategies, feedback loops, observability, and data governance High ownership mindset, comfortable operating in fast-moving and ambiguous environments