Principal Platform Engineer - People Systems

At Apple, we believe the people who build our products deserve systems built with that same care. That belief is what drives People Systems. Behind every revolutionary hardware launch, breakthrough service, and magical customer experience are the passionate people who make it all possible.

This is our pursuit: to be the unseen force behind a world-class employee experience for the most exceptional people on Earth — realized through obsessively crafting and caring for the systems that hire, reward, and support our talent as they grow, thrive, and do the best work of their lives.

Sitting at the vibrant intersection of technology, people, and business, we architect the platforms behind every stage of that journey: Recruiting, Onboarding, Core HR, Payroll, Compensation, and Benefits. Our systems don’t just process transactions; they shape how people experience working here.

When you engineer for People Systems, your users are Apple’s most vital asset: its People. We are seeking a visionary Application Architect to shape the technical strategy for People Systems with a passion for platforms where configuration is the product, and agentic workflows that redefine what's possible..

You will be working on a Platform that helps define features. Data models, rules, workflows, permissions, screens and reports are all versioned configuration, interpreted at runtime, so the people closest to a problem can change how the system behaves without waiting for a release. Hundreds of components must behave as one system. We are looking for an Application Architect who can make that true.

We value engineers who bring both technical excellence and product thinking. You'll make the architectural calls that balance fast delivery against long-term platform health, and build systems that are scalable, secure and resilient by design rather than by retrofit. This role requires someone who thrives on complexity but ships simplicity.

Collaboration is central to how we work. You’ll partner with product managers and cross-functional engineering teams to understand problems deeply before reaching for solutions. The best engineers here are the ones who ask “why” before “how". If you care deeply about precision, data integrity, and crafting platforms where scale meets human empathy, come join us to do the best work of your life.

Minimum Qualifications

15+ years of professional software engineering in system design, technical architecture or solution architecture for large-scale enterprise solutions; BS/MS in Computer Science, Software Engineering or a related field, or equivalent practical experience
Led the architecture of a configuration-driven platform used by multiple engineering teams: metadata models, multi-tenancy, extensibility, versioned APIs, and safe publish, promote and rollback of configuration
Proficient across technology stacks rather than tied to one: has built components of very different character in one system (interpreters or compilers of declarative input, query and authorization layers, workflow and state engines, search and reporting, UI runtimes), picking up whatever language, framework or datastore each demands, with the judgement to apply the right guarantees to each
Distributed-systems depth: consistency models, fault tolerance, partitioning and failure-mode analysis; designed for high availability and horizontal scale under variable load, serving millions of users and high-volume transactions
Strong foundation in event-driven and event-sourced architecture: saga and workflow orchestration, streaming platforms, change data capture and transactional data stores
Evolved schemas and contracts at scale: backward-compatible change of metadata and configuration models, breaking-change detection, and migration of in-flight data and workflows
Deep proficiency in Spring Boot (or equivalent) and microservices patterns, with async fluency: virtual threads (Java 21+), coroutines or reactive programming
Expert use of relational or NoSQL databases at scale: schema design, query optimization, migrations and transaction management
Hands-on experience with an AI-native SDLC and agent orchestration frameworks: working with coding agents under tests and review, and building with tool calling, graph-based orchestration, context management and structured-output validation
Cloud-native production experience on AWS or comparable platforms (Kubernetes, identity and access management, cost governance), operating with monitoring, alerting, incident response and a post-mortem culture
Set technical direction across multiple teams without direct authority: break down ambiguous problems without detailed specs, translate business requirements into designs, and explain trade-offs clearly to engineers and executives

Preferred Qualifications

Built expression languages, rule engines or policy engines (typed ASTs, multiple execution backends), and the differential and conformance testing that keeps those backends consistent
Experience decomposing or extending monolithic enterprise systems into service-based platforms that interoperate with the existing system of record while the business ran uninterrupted
Background in CQRS, event sourcing or saga-based orchestration for long-running workflows
Familiarity with compliance and audit requirements for sensitive personal data (PII, regulated HR data), and AI governance: drift detection, bias monitoring, explainability and audit trails
Experience putting LLMs and ML into production backend services: inference APIs, RAG pipelines, vector search, embeddings, evaluation and observability tooling, prompt and context engineering
Proven record of leading technical decisions cross-functionally, aligning non-engineering stakeholders on trade-offs, and moving engineering culture toward measurable reliability, correctness, cost ownership and evaluation-driven development
Designed APIs consumed by many teams (versioning, contract stability, documentation) and integrated with enterprise platforms through APIs, ETL or event streams amid many upstream and downstream dependencies
Developer experience for platform consumers: SDKs, local test harnesses, simulation environments and self-service onboarding
Fluency with AI-assisted development tools, and AI-augmented operations such as predictive alerting, automated root-cause analysis and self-healing runbooks
Designed evaluation, guardrail and human-in-the-loop strategies for AI systems that make or inform consequential decisions
Hands-on chaos engineering and advanced reliability practice: failure injection, game days, capacity modeling and traffic shaping