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

Apple•Cupertino, CA

About The Position

Apple Services Engineering (ASE) builds AI and machine learning experiences across services including the App Store, Apple Music, and Apple TV. The Engineering Leader for AI Safety Engineering & Evaluation will lead this function within the Human-Centered AI organization, developing scalable capabilities to identify, measure, and mitigate risks associated with AI-powered experiences. The role combines software engineering, machine learning, evaluation science, and Responsible AI, with a focus on turning manual safety assessments into automated, scientifically rigorous processes. The leader will shape the technical foundations and organizational capabilities of a growing, multidisciplinary team of engineering managers, engineers, and applied scientists. They will set technical direction, develop engineering practices, and guide safety-specific modeling and automated assessments. In partnership with Evaluation Systems & Research (ESR), the role will integrate safety models, workflows, and measurement capabilities into shared, scalable evaluation systems. The position works closely with machine learning engineering, research, product, privacy, legal, and safety policy teams. It translates safety requirements into measurable technical standards and actionable insights that inform AI development and deployment decisions. The leader will establish a multi-year roadmap, oversee staffing and delivery, provide technical guidance, and communicate evaluation findings and emerging risks to senior stakeholders.

Requirements

12+ years of experience in software engineering, machine learning engineering, applied AI, or related technical disciplines, including 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, 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 the 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 existing 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

Develop and execute the technical strategy and multi-year roadmap for AI safety engineering and evaluation.
Build, scale, and mentor a multidisciplinary organization of engineering managers, software engineers, machine learning engineers, and applied scientists; oversee staffing, resource planning, performance management, and delivery commitments.
Provide hands-on technical direction for safety-specific modeling and automated assessments, and establish engineering standards, review practices, and operating mechanisms.
Lead development of automated, model-based evaluation approaches—including LLM-as-a-judge, multi-model assessment, and automated classification—to assess harmful content, bias, robustness, and Responsible AI risks.
Establish rigorous evaluation methodologies, benchmarks, and experimental metrics to measure model behavior and mitigation effectiveness.
Advance synthetic data generation, scenario-based testing, and automated red teaming to find systematic failure patterns and characterize emerging safety risks.
Direct implementation of safety evaluation workflows, model-based assessors, automated testing logic, and risk-analysis pipelines.
Partner with Evaluation Systems & Research (ESR) to integrate domain-specific safety capabilities into shared evaluation platforms and embed repeatable automated assessments in model development, experimentation, and release workflows.
Establish practices for evaluation data management, benchmark quality, test coverage, and continuous measurement; manage technical dependencies and delivery priorities across partner engineering teams.
Partner with AI/ML engineering, research, product, privacy, legal, and safety policy teams to translate safety and governance requirements into measurable technical criteria and evaluation capabilities.
Influence AI development and product release decisions through evidence-based assessments of model capabilities, limitations, and mitigation effectiveness.
Communicate technical strategy, evaluation results, and emerging risks to senior engineering, product, and executive stakeholders.
Establish cross-functional operating models that embed automated safety assessments into engineering workflows and reduce reliance on manual expert reviews.