Engineering Leader, AI Safety Engineering & Evaluation, Services Engineering
Job in
Cupertino, Santa Clara County, California, 95015, USA
Listed on 2026-11-01
Listing for:
Apple
Full Time
position Listed on 2026-11-01
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Software Project Mgr/ Lead
Job Description & How to Apply Below
Role Number:
Team:
Software and Services
Location:
Cupertino Weekly
Hours:
40
Posted:
Summary Apple Services Engineering (ASE) powers AI and machine learning experiences across the App Store, Apple Music, Apple TV, and more. We build intelligent, deeply personal experiences designed to serve users around the world. Ensuring these experiences are safe, trustworthy, and representative of the diverse communities we serve is fundamental to how we develop and deploy AI.We are seeking an experienced engineering leader to lead AI Safety Engineering & Evaluation within the Human-Centered AI organization.
This role combines software engineering, machine learning, evaluation science, and Responsible AI to develop scalable capabilities for identifying, measuring, and mitigating risks associated with AI-powered experiences.
You will lead an emerging, multidisciplinary organization of engineering managers, engineers, and applied scientists, building the technical expertise, engineering practices, and organizational capabilities needed to scale AI safety evaluation. This is an opportunity to shape a growing function, developing its technical foundations while transforming manual safety assessments into automated, scientifically rigorous processes.
The ideal candidate brings strong engineering leadership, practical expertise in machine learning and modern AI systems, and demonstrated success building technical organizations and translating complex challenges into measurable engineering outcomes.
Description As the Engineering Leader for AI Safety Engineering & Evaluation, you will lead the development and operationalization of technical capabilities to systematically assess AI model behavior, identify potential harms, and measure the effectiveness of safety mitigations.
Your organization will advance safety evaluation methodologies, model-based assessment, adversarial testing, automated risk identification, and failure analysis. In partnership with Evaluation Systems & Research (ESR), which provides shared evaluation infrastructure and services, you will integrate safety-specific models, assessment workflows, and measurement capabilities into scalable evaluation systems.
You will establish the technical direction, organizational structure, and engineering practices needed for a growing team to operate effectively. This requires a leader who can engage deeply in technical decisions, develop engineering managers and senior contributors, and progressively build the organization's ability to deliver complex technical solutions.
You will work closely with machine learning engineering, research, product, privacy, legal, and safety policy teams to translate safety requirements into measurable technical standards and actionable insights that inform AI development and deployment decisions.
Minimum Qualifications
12+ years of experience in software engineering, machine learning engineering, applied AI, or related technical disciplines, including demonstrated experience delivering complex production-quality systems.
8+ years of engineering or applied machine learning management experience, including at least 3 years managing engineering managers or leading multiple technical teams through established managers.
Demonstrated experience building or transforming engineering organizations, including developing technical talent, establishing engineering practices, and scaling teams through periods of organizational or technical growth.
Strong software engineering foundation, including experience guiding architecture, implementation, and operationalization of complex software or AI/ML solutions.
Practical expertise in machine learning and modern AI architectures, including model development, inference, and evaluation…
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