Software Engineer, Applied AI

Full-stack builders, come join a creative engineering team devoted to making our products more durable through data-driven insights. Were looking for an Applied AI engineer to develop intelligent applications and systems that unlock the power of our hardware test data and enable hardware engineers to create their own software tools. In this role, youll build scalable software to help take our departments capabilities to the next level. Youll work with hardware and software engineering teams throughout Apple to design robust AI/ML applications, implement production-grade software and AI pipelines, iterate based on evolving requirements, and provide input on technical strategy. The tools and platforms you build will power processes, analytics, and workflows that directly influence the design of future products. This is a hands-on work environment where engineers are expected to be self-motivated and proficient with a wide range of AI/ML technologies, while dedicating time to supporting contractors, driving projects forward, presenting to leadership, and delivering excellent solutions for Apple.

In this role youll implement AI/ML software applications at cloud-scale for the Reliability department at Apple. You'll bridge the gap between business needs and production software, delivering tools that automate workflows and surface novel insights for the organization.

Implement robust, full-stack GenAI-oriented applications using modern software engineering best practices, including a Python/FastAPI backend, a React/Next.js TypeScript frontend, AWS RDS and ElastiCache data stores, and containerized deployments on AWS EKS with Helm and Terraform. Extend and maintain a multi-tenant EKS platform that enables users to build, deploy, and run their own applications in Kubernetes pods, with integrated build pipelines, sidecars, and session-affinity reverse proxy at 5,000+ pod scale. Design and integrate GenAI capabilities into the platform, including agentic workflows, AI agent skills, LLM-powered web features, and production-grade inference pipelines. Drive the scope of engineering projects moving through the full project lifecycle from concept to deployment Collaborate with a team of external contractors, providing code reviews and support to ensure fast, high-quality delivery. Implement and operate production observability with OpenTelemetry tracing, Prometheus metrics, and structured logging to ensure application reliability and performance.B.S. in Computer Science, Software Engineering, Computer Engineering, Machine Learning, or related field. Exposure to software engineering through internships, with strong foundation in CS fundamentals, including data structures, algorithms, and exposure to building web applications using Python (FastAPI, SQLAlchemy), TypeScript (React/Next.js), and containerized services on Kubernetes (EKS, Helm, Terraform). Exposure to applied AI Engineering, building software leveraging GenAI and ML to create production-level solutions to business needs, and enhance organizational and development workflows. Ability to follow direction from engineering lead for feature scope, ability to collaborate with stakeholders, review and refine requirements, moving project towards completion. Strong communication and presentation skills, with the ability to articulate technical concepts to diverse audiences while thriving in a fast-paced, evolving environment. Passion for quality and attention to detail; proactive in researching and assessing emerging technologies (AI/ML models, protocols, and techniques), and integrating them into production. Exposure to multi-tenant platforms that execute user-submitted code, including container image builds, workload isolation, RBAC systems, and secure callback architectures is a plus. Experience building or integrating agentic AI systems, LLM tool-use patterns, or AI-assisted development workflows. Exposure to production observability stacks: OpenTelemetry, Prometheus, structured logging, distributed tracing, and dashboarding tools such as Grafana. Track record of successfully growing engineering projects from initial proof-of-concept to organization-wide production tools. Strong foundation in Software Engineering fundamentals, including data structures, algorithms, object-oriented design, through school projects or internships. Experience with computer vision technologies and techniques, especially for segmentation, anomaly detection, and objective grading is a plus.