Software Engineer, Applied AI
Apple
- Location
- Onsite (San Diego, California)
- Employment
- Full-time
- Level
- Entry Level
Posted 4 days ago
About the Role
Apple's Reliability department is seeking an Applied AI Engineer to build scalable, production-grade AI/ML applications that automate workflows and generate insights from hardware test data. This role involves collaborating with cross-functional teams to design robust software solutions that directly influence the design of future products.
Skills
Python
FastAPI
SQLAlchemy
TypeScript
React
Next.js
Kubernetes
Amazon EKS
Helm
Terraform
Applied AI
Machine Learning
Generative AI
Agentic AI Systems
Computer Vision
OpenTelemetry
Full job details
Full-stack builders, come join a creative engineering team devoted to making our products more durable through data-driven insights. We're 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, you'll 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 supporting contractors, driving projects forward, presenting to leadership, and delivering excellent solutions for Apple.
In this role you'll 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.
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.
Description
In this role you'll 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.
Minimum Qualifications
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.
Preferred Qualifications
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.