Engineering Leader, AI Safety Engineering & Evaluation, Apple Services Engineering

Apple•Cupertino, CA

About The Position

Apple Services Engineering (ASE) powers AI and machine learning experiences across services including the App Store, Apple Music, and Apple TV. The organization aims to build intelligent, personal experiences that are safe, trustworthy, and representative of the diverse communities it serves. The Engineering Leader for AI Safety Engineering & Evaluation will lead this function within the Human-Centered AI organization, combining software engineering, machine learning, evaluation science, and Responsible AI to develop scalable capabilities for identifying, measuring, and mitigating risks in AI-powered experiences. The role leads an emerging multidisciplinary organization of engineering managers, engineers, and applied scientists. The leader will develop its technical expertise, engineering practices, and organizational capabilities, and help transform manual safety assessments into automated, scientifically rigorous processes. The leader will develop and operationalize technical capabilities to assess AI model behavior, identify potential harms, and measure safety-mitigation effectiveness. The team 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, the leader will integrate safety-specific models, workflows, and measurement capabilities into scalable evaluation systems. The role establishes technical direction, organizational structure, and engineering practices; engages in technical decisions; develops engineering managers and senior contributors; and builds the organization’s ability to deliver complex solutions. The leader works with machine learning engineering, research, product, privacy, legal, and safety policy teams to translate safety requirements into measurable technical standards and actionable insights for AI development and deployment decisions.

Requirements

12+ years of experience in software engineering, machine learning engineering, applied AI, or related technical disciplines, including demonstrated delivery of complex production-quality systems.
8+ years of engineering or applied machine learning management experience.
At least 3 years managing engineering managers or leading multiple technical teams through established managers.
Demonstrated experience building or transforming engineering organizations, developing technical talent, establishing engineering practices, and scaling teams through 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 methodologies.
Strong technical foundation in Python and modern software development practices, with ability to assess technical tradeoffs and guide engineering execution.
Demonstrated ability to develop technical roadmaps, establish priorities, manage resources, and deliver measurable outcomes in ambiguous environments.
Excellent communication and stakeholder management skills, including experience influencing senior technical and cross-functional leaders.
Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field, or equivalent practical experience.

Nice To Haves

Experience leading teams developing AI/ML evaluation systems, automated model assessment, or applied machine learning solutions.
Experience with generative AI and large language models, including LLM-based evaluation, agentic systems, model orchestration, or adversarial testing.
Applied experience with machine learning modeling, statistical analysis, experimental design, and quantitative evaluation methods.
Experience developing automated model assessors, synthetic data generation, red teaming, or systematic failure analysis capabilities.
Familiarity with Responsible AI principles and technical approaches to fairness, bias, robustness, harmful content, and safety risk assessment.
Experience integrating evaluation capabilities into engineering platforms, development workflows, or continuous measurement processes.
Experience leading multidisciplinary engineering organizations through technical transformation, particularly involving automation or applied machine learning.
Ability to translate emerging research techniques into reliable engineering implementations.
Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical discipline.

Responsibilities

Lead AI Safety Engineering & Evaluation within the Human-Centered AI organization.
Build and lead a multidisciplinary organization of engineering managers, engineers, and applied scientists.
Develop scalable technical capabilities to assess AI model behavior, identify potential harms, and measure the effectiveness of safety mitigations.
Advance safety evaluation methodologies, model-based assessment, adversarial testing, automated risk identification, and failure analysis.
Integrate safety-specific models, assessment workflows, and measurement capabilities into scalable evaluation systems in partnership with Evaluation Systems & Research (ESR).
Establish the organization’s technical direction, structure, and engineering practices.
Engage in technical decisions and develop engineering managers and senior technical contributors.
Build the organization’s capacity to deliver complex technical solutions and transform manual safety assessments into automated processes.
Partner with machine learning engineering, research, product, privacy, legal, and safety policy teams.
Translate safety requirements into measurable technical standards and actionable insights that inform AI development and deployment decisions.
Develop technical roadmaps, priorities, and resource plans, and deliver measurable outcomes in ambiguous environments.