Summary
Lead AI/ML Engineer to drive advancements in intelligent infrastructure operations using GenAI and agentic technologies. Focus on building practical, high-impact AI systems to improve engineering workflows and decision-making. Responsibilities include identifying operational problems, setting architecture direction, designing agentic solutions, and guiding teams from prototype to production. Aims to combine LLM reasoning with automation and safeguards for enhanced speed, reliability, and efficiency. Success measured by adoption of shared patterns, toil reduction, and cost savings.
Description
The Lead AI/ML Engineer will play a critical role in advancing Apple’s next generation of intelligent infrastructure operations through applied GenAI and agentic technologies. This role focuses on building practical, high-impact AI systems that improve engineering workflows and infrastructure decision-making. You will identify high-leverage operational problems, set architecture direction, design agentic solutions, and guide teams from prototype to production adoption. The goal is combining LLM reasoning, system context, automation frameworks, and engineering safeguards to improve speed, reliability, and efficiency. Success in this role will be measured by concrete outcomes: adoption of shared patterns and tools by multiple teams, measurable toil reduction, validated cost or capacity savings. You will help define how AI can safely and effectively augment engineering teams—from capacity optimization and deployment analysis to incident response, forecasting, and infrastructure planning.
5+ years of experience in software engineering, infrastructure engineering, or large-scale cloud services environments
Proven experience designing, building, or technically leading production GenAI, ML platform, developer productivity, infrastructure automation, or tooling systems
Hands-on experience with GenAI technologies, LLM application architecture, including retrieval, context engineering, tool use, workflow orchestration, agentic workflows, evaluation, observability, and failure handling
Demonstrated technical leadership across teams, including architecture reviews, roadmap influence, mentoring, and driving adoption of shared engineering practices
Strong understanding of cloud infrastructure operations, observability, deployment systems, and operational safety principles
Proven ability to translate ambiguous operational challenges into practical engineering solutions with measurable business impact
Strong software development skills in Python, Java, or similar languages
Exceptional analytical, systems thinking, and cross-functional communication skills
Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical field
Experience applying GenAI to infrastructure operations, SRE workflows, capacity planning, or engineering productivity systems
Experience building AI systems with operational guardrails, governance models, and safe deployment patterns for enterprise environments
Strong understanding of capacity forecasting, cost optimization, and infrastructure efficiency modeling at hyperscale
Background working in private cloud environments, large-scale storage systems, or global distributed infrastructure
PhD or advanced degree in Computer Science, Machine Learning, Distributed Systems, or related field
