Principal Platform Engineer - People Systems

Apple•Sunnyvale, CA

About The Position

Apple’s People Systems team builds and operates platforms supporting the employee experience across recruiting, onboarding, core HR, payroll, compensation, and benefits. The Principal Platform Engineer will shape technical strategy for configuration-driven People Systems platforms, where versioned data models, rules, workflows, permissions, screens, and reports are interpreted at runtime. The role calls for strong technical judgment and product thinking to build scalable, secure, resilient systems while balancing delivery speed with long-term platform health. The engineer will define application architecture and patterns across varied components, establish coherent configuration and code models, and ensure data integrity, consistency, and privacy in sensitive domains. They will own system design for new initiatives, design stable APIs and domain-driven services, lead implementation and testing, and guide reliability practices. The role also includes establishing architecture standards, advising partner teams, communicating strategy and trade-offs to leadership, and mentoring engineers across teams. The position includes developing database-management automation and API-driven services; integrating AI/ML capabilities into configuration recommendations, workload scheduling, and operator interfaces; incorporating automation into CI/CD pipelines; and building monitoring, alerting, health checks, and self-healing mechanisms. The role also advances AI-native software development and agent orchestration, including specification, evaluation, sandboxing, validated outputs, and deterministic fallbacks.

Requirements

15+ years of professional software engineering experience 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.
Experience leading architecture of a configuration-driven platform used by multiple engineering teams, including metadata models, multi-tenancy, extensibility, versioned APIs, and safe configuration publishing, promotion, and rollback.
Broad experience across technology stacks, including components such as interpreters or compilers, query and authorization layers, workflow and state engines, search and reporting, or UI runtimes, with sound judgment about suitable guarantees.
Deep distributed-systems knowledge, including consistency models, fault tolerance, partitioning, and failure-mode analysis, with experience designing for high availability and horizontal scale under variable load, millions of users, and high-volume transactions.
Strong foundation in event-driven and event-sourced architecture, including saga and workflow orchestration, streaming platforms, change data capture, and transactional data stores.
Experience evolving schemas and contracts at scale, including backward-compatible metadata and configuration changes, breaking-change detection, and migration of in-flight data and workflows.
Deep proficiency in Spring Boot or an equivalent framework and microservices patterns, with asynchronous programming experience such as Java 21+ virtual threads, coroutines, or reactive programming.
Expertise with relational or NoSQL databases at scale, including schema design, query optimization, migrations, and transaction management.
Hands-on experience with AI-native software development and agent orchestration frameworks, including coding agents used with tests and review, tool calling, graph-based orchestration, context management, and structured-output validation.
Cloud-native production experience on AWS or comparable platforms, including Kubernetes, identity and access management, cost governance, monitoring, alerting, incident response, and post-mortem practices.
Ability to set technical direction across multiple teams without direct authority, break down ambiguous problems, translate business requirements into designs, and explain trade-offs to engineers and executives.
Experience building expression, rule, or policy engines, including typed abstract syntax trees, multiple execution backends, and differential or conformance testing.
Experience decomposing or extending monolithic enterprise systems into service-based platforms that interoperate with an existing system of record while business operations continue.
Background in CQRS, event sourcing, or saga-based orchestration for long-running workflows.
Familiarity with compliance and audit requirements for sensitive personal data, including PII or regulated HR data, and AI governance such as drift detection, bias monitoring, explainability, and audit trails.
Experience putting LLMs and ML into production backend services, including inference APIs, RAG pipelines, vector search, embeddings, evaluation and observability tooling, and prompt and context engineering.
Proven record of leading cross-functional technical decisions, aligning non-engineering stakeholders on trade-offs, and advancing measurable reliability, correctness, cost ownership, and evaluation-driven development.
Experience designing APIs consumed by many teams, including versioning, contract stability, and documentation, and integrating enterprise platforms through APIs, ETL, or event streams amid upstream and downstream dependencies.
Experience improving platform-consumer developer experience through SDKs, local test harnesses, simulation environments, or 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.
Experience designing evaluation, guardrail, and human-in-the-loop strategies for AI systems that make or inform consequential decisions.
Hands-on experience with chaos engineering and advanced reliability practices, including failure injection, game days, capacity modeling, and traffic shaping.

Responsibilities

Architect applications configuration-first, determine what belongs in versioned configuration versus code, maintain a coherent model, and communicate platform gaps as requirements.
Choose appropriate design patterns and guarantees across components such as compiled kernels, search, UI, and AI services.
Establish core application patterns, including a canonical document model, saga-based orchestration, and separation of recorded facts from decisions made about them.
Ensure data integrity, consistency, and privacy in high-stakes domains, enforcing safe access at read and write boundaries.
Own end-to-end system design for new business initiatives, including systems impact analysis across the existing platform ecosystem.
Design APIs with strong contracts and clear versioning for numerous consuming teams.
Lead the design and coding of modular, domain-driven services using Java or Kotlin, Spring Boot, cloud-native infrastructure, and event streams; set testing strategy and uphold code-review standards.
Shape AI-native software development and agent orchestration for platform engineering and application use cases, including tool interfaces, structured-output validation, and deterministic fallbacks.
Define and uphold reliability practices, including SLOs, observability, incident response, zero-downtime deployments, distributed-systems troubleshooting, and high-volume pipeline tuning.
Establish architecture standards, reference designs, and platform blueprints; guide partner teams in platform design and integration.
Present technical strategy, modernization options, capacity, risk, and technical-debt retirement to leadership, and drive decisions amid ambiguity.
Mentor engineers and strengthen technical capacity across teams.
Design and build automation tools for database management and performance tuning; integrate AI/ML for configuration recommendations, workload-aware scheduling, and LLM-powered operator workflows.
Design, build, and maintain secure, versioned microservices exposing database lifecycle operations through APIs; handle concurrency and partial failures, and evolve APIs compatibly.
Integrate database-management automation into CI/CD pipelines with automated testing, canary rollouts, and rollback capabilities.
Develop monitoring, alerting, and health-check systems for database health visibility, and build mechanisms to detect and remediate known failures automatically.