AI Agent Infrastructure Engineer – ML Systems

Apple Inc.; Cupertino, Northern (CA, KY); Hybrid; USD 185,000 - 325,000; Full time

Benefits offered by this job

Apple employee stock programs

Relocation assistance

Educational expense reimbursement

Comprehensive medical and dental

Retirement benefits

Discounted products and services

Employee stock purchase plan

Job summary

Apple Inc. in Cupertino, CA is seeking an Agent Sciences and Infrastructure Engineer, AIML to build agentic systems for prototyping, optimizing, and shipping Apple Intelligence features.

You will join a team focused on large language modeling, agent harnesses, evaluation, and pipelines that connect training, adaptation, and deployment. The role requires strong software and systems background, experience with ML or agent infrastructure end-to-end, and the ability to balance speed with long-term

Qualifications

• Strong Python and UNIX skills with expertise using agentic coding tools.
• Experience designing, building, and operating ML infrastructure—training/evaluation pipelines, data and experiment tooling, serving, or agent harnesses.
• Independently scope and lead complex, multi-month engineering projects from ambiguity to production systems.
• Proven ability to define goals and deliver results amid uncertainty in AI product development.
• Proven track record of driving engineering efforts to completion despite obstacles.

Skills

Python

UNIX

Distributed systems

APIs

Testing

Project leadership

AI product development

Agent frameworks

Education

BS / MS / PhD in CS/SE or related

Job description

Apple Inc. in Cupertino, CA is seeking an Agent Sciences and Infrastructure Engineer, AIML to build agentic systems for prototyping, optimizing, and shipping Apple Intelligence features.

You will join a team focused on large language modeling, agent harnesses, evaluation, and pipelines that connect training, adaptation, and deployment. The role requires strong software and systems background, experience with ML or agent infrastructure end-to-end, and the ability to balance speed with long-term