Software Engineer, Applied AI, Tools & Infrastructure

Full-stack builders, come join a creative engineering team devoted to making our products more durable through data-driven insights. We're looking for a senior Applied AI engineer to lead the development of 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, you'll architect and build scalable software to help take our department's capabilities to the next level. You'll 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 leading contractors, driving projects forward, presenting to executive leadership, and delivering excellent solutions for Apple.

Description

In this role you'll architect and implement AI/ML software applications at cloud-scale for the Reliability department at Apple. You’ll provide technical leadership and bridge the gap between business needs and production software, delivering tools that automate workflows and surface novel insights for the organization.","responsibilities":"Architect and build 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, while providing technical leadership across projects

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 independently, identifying new opportunities, defining requirements, and managing the full project lifecycle from concept to deployment

Lead and mentor a team of external contractors, providing architectural guidance, code reviews, and daily direction 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

Cultivate strong partnerships across engineering and business teams, acting as both a visionary and translator between software and non-software audiences

Act as the primary point of contact for stakeholders, tailoring communication to multidisciplinary audiences while delivering regular updates on project roadmaps

Present complex technical narratives and data-driven insights effectively to executive leadership through clear, high-impact presentations and visualizations

Preferred Qualifications

M.S. in Computer Science, Software Engineering, Computer Engineering, Machine Learning, or related field

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

Deep expertise in Kubernetes networking (NetworkPolicies, ingress, service mesh, sidecars, pod-to-pod TLS), particularly in enterprise environments with corporate proxies and WAFs

Strong experience building multi-tenant platforms that execute user-submitted code, including container image builds, workload isolation, RBAC systems, and secure callback architectures

Experience building or integrating agentic AI systems, LLM tool-use patterns, or AI-assisted development workflows

Experience with production observability stacks: OpenTelemetry, Prometheus, structured logging, distributed tracing, and dashboarding tools such as Grafana

Track record of successfully growing the scope of engineering projects from initial proof-of-concept to organization-wide adoption

Strong foundation in Software Engineering fundamentals, including data structures, algorithms, object-oriented design, and proficiency in building production-quality applications

Experience with computer vision technologies and techniques, especially for segmentation, anomaly detection, and objective grading is a plus

Minimum Qualifications

B.S. in Computer Science, Software Engineering, Computer Engineering, Machine Learning, or related field

3+ years software engineering experience with strong foundation in CS fundamentals, including data structures, algorithms, and proficiency building production web applications using Python (FastAPI, SQLAlchemy