Senior​/Software Engineer, Apple Cloud AI Platform

Senior / Staff Software Engineer, Apple Cloud AI Platform

Seattle, Washington, United States Software and Services

Apple Cloud AI Platform powers the machine learning, AI, and data systems that enable intelligent experiences across Apple consumer products. We are looking for a Product Engineer who is excited to work directly with Apple product teams and translate real‑world AI needs into production‑ready solutions. You will operate at the intersection of full‑stack engineering, data and ML platforms, backend systems and applied AI, using customer requirements to build end‑to‑end solutions and workflows as part of our platform offerings.

This role is ideal for engineers who are highly collaborative, proactive, adaptable, and capable of turning ambiguous customer requests into working AI‑powered solutions at scale.

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.

• Build products, solutions, experience, tools, demos, and reference implementations to accelerate Apple Cloud AI platform adoption and solve customer use cases
• Influence platform roadmap based on customer lifecycle needs (developer experience, scale, reliability, governance, ML metadata).
• Build advanced ML workflows such as distributed training, tuning, feedback loops, observability, and evaluation pipelines
• Partner with customer teams to understand ML product requirements, identify integration challenges, and drive cross‑lifecycle technical decisions (data, training, evaluation, deployment, monitoring).
• Design and build end‑to‑end ML workflows and services spanning data ingestion, feature computation, training, evaluation, inference, and user‑facing surfaces.
• Develop backend services and interfaces (APIs, CLIs, SDKs, or UIs) supporting scalable ML workloads.
• Integrate with platform systems including orchestration, storage, training/evaluation services, authentication, and monitoring.
• Build developer platforms, products and experiences (SDKs, developer tools, agents, internal platforms).
• Ability to represent customer needs to platform engineering and drive cross‑team technical direction.

• 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, Graph
  
  QL, 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

• Strong understanding of the ML lifecycle — experiment tracking, model packaging,…