Senior / Staff Software Engineer, Apple Cloud AI Platform

Apple Apple · Big Tech · Seattle, WA · Software and Services

Product Engineer for Apple Cloud AI Platform, focused on building end-to-end AI-driven systems and solutions for internal customers, translating real-world AI needs into production-ready platform offerings. This role involves working across the full stack, from data ingestion to model execution and UI integration, influencing platform roadmaps, and driving alignment between product management, partner platforms, and customer teams.

What you'd actually do

  1. Build products, solutions, experience, tools, demos, and reference implementations to accelerate Apple Cloud AI platform adoption and solve customer use cases
  2. Influence platform roadmap based on customer lifecycle needs (developer experience, scale, reliability, governance, ML metadata).
  3. Build advanced ML workflows such as distributed training, tuning, feedback loops, observability, and evaluation pipelines
  4. Partner with customer teams to understand ML product requirements, identify integration challenges, and drive cross-lifecycle technical decisions (data, training, evaluation, deployment, monitoring).
  5. Design and build end-to-end ML workflows and services spanning data ingestion, feature computation, training, evaluation, inference, and user-facing surfaces.

Skills

Required

  • 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

Nice to have

  • 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

What the JD emphasized

  • production systems
  • full-stack applications
  • data workflows
  • ML-powered products
  • platform tooling
  • modern AI and data infrastructure
  • cloud environments
  • distributed systems
  • containers
  • CI/CD pipelines
  • developer platforms and tooling
  • SDKs
  • CLIs
  • agents
  • internal tools
  • ambiguous customer requirements
  • clear technical direction
  • cross-team alignment
  • ML lifecycle
  • experiment tracking
  • model packaging
  • distributed training
  • evaluation pipelines
  • deployment strategies
  • feedback loops
  • observability
  • data governance

Other signals

  • customer requirements
  • production-ready solutions
  • end-to-end solutions
  • scale
  • developer tooling
  • platforms
  • systems
  • experiences
  • internal customers
  • AI-driven systems
  • Apple Cloud AI Platform capabilities
  • data ingestion
  • model execution
  • UI integration
  • prototype quickly
  • harden solutions for production
  • build services
  • feed insights back to platform teams
  • influence roadmap
  • improve the developer experience
  • bridge between product management, partner platform, and customer teams
  • define best practices
  • documenting patterns
  • drive alignment
  • deliver systems
  • strong engineering fundamentals
  • applied ML awareness
  • platform thinking
  • customer empathy
  • deliver in fast-evolving environments
  • accelerate Apple Cloud AI platform adoption
  • solve customer use cases
  • Influence platform roadmap
  • developer experience
  • scale
  • reliability
  • governance
  • ML metadata
  • customer teams
  • ML product requirements
  • integration challenges
  • cross-lifecycle technical decisions
  • data
  • training
  • evaluation
  • deployment
  • monitoring
  • end-to-end ML workflows
  • services
  • data ingestion
  • feature computation
  • training
  • evaluation
  • inference
  • user-facing surfaces
  • backend services
  • interfaces
  • APIs
  • CLIs
  • SDKs
  • UIs
  • scalable ML workloads
  • platform systems
  • orchestration
  • storage
  • training/evaluation services
  • authentication
  • monitoring
  • developer platforms
  • products
  • experiences
  • SDKs
  • developer tools
  • agents
  • internal platforms
  • customer needs to platform engineering
  • cross-team technical direction
  • production systems
  • full-stack applications
  • data workflows
  • ML-powered products
  • platform tooling
  • modern frontend frameworks
  • developer-facing UIs
  • internal platform tooling
  • Python
  • modern AI and data infrastructure
  • Spark
  • Ray
  • gRPC
  • GraphQL
  • REST
  • Kafka
  • cloud environments
  • distributed systems
  • containers
  • CI/CD pipelines
  • developer platforms
  • tooling
  • SDKs
  • CLIs
  • agents
  • internal tools
  • ambiguous customer requirements
  • clear technical direction
  • cross-team alignment
  • ML lifecycle
  • experiment tracking
  • model packaging
  • distributed training
  • evaluation pipelines
  • deployment strategies
  • feedback loops
  • observability
  • data governance
  • High ownership mindset
  • fast-moving and ambiguous environments