AI Agent Infrastructure Engineer – ML Systems & Deployment

Apple Inc.; Cupertino (CA); On-site; USD 175,000 - 309,000; Full time

Job summary

Apple is seeking an Agent Sciences and Infrastructure Engineer, AIML, in Seattle, WA, to design and build agentic systems for prototyping, optimization, and deployment of Apple Intelligence features. You will work with ML researchers and engineers on large language models, agent harnesses, and end-to-end pipelines spanning training, adaptation, and deployment.

The role requires a strong software and systems engineering background with hands-on experience in ML infrastructure, end-to-end tooling,

Qualifications

• Independent ly scope and lead complex, multi-month engineering projects, from an ambiguous starting point through to a production system
• Proven ability to define goals and deliver results amid uncertainty and real-world constraints in AI product development
• Experience designing, building, and operating infrastructure for machine learning - training/evaluation pipelines, data and experiment tooling, serving, or agent harnesses
• Strong software engineering fundamentals: distributed systems, APIs, testing, and reliable, maintainable code
• Strong Python and UNIX skills and a demonstrated ability to use agentic coding tools in these environments
• Proven track record of driving engineering efforts to completion while overcoming obstacles and ambiguity
• BS and 5+ years of experience, MS and 3+ years of experience, or PhD and 1+ year of experience

Skills

Python

UNIX

Distributed systems

APIs

Testing

Agent harnesses

Leadership

Education

BS in CS or related field

Job description

Apple is seeking an Agent Sciences and Infrastructure Engineer, AIML, in Seattle, WA, to design and build agentic systems for prototyping, optimization, and deployment of Apple Intelligence features. You will work with ML researchers and engineers on large language models, agent harnesses, and end-to-end pipelines spanning training, adaptation, and deployment.

The role requires a strong software and systems engineering background with hands-on experience in ML infrastructure, end-to-end tooling,