Engineering Leader, AI Safety Engineering & Evaluation, Apple Services Engineering
Apple · Location Cupertino
- Engineering Leader, AI Safety Engineering & Evaluation, Apple Services Engineering
- Cupertino, California, United States
- Software and Services
- Submit Resume
- Summary
- Posted:
- Oct 16, 2026
- Weekly Hours:
- 40
- Role Number:
- 200689831-0836
- 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.
- Responsibilities
- Technical Strategy & Organizational Leadership: Develop and execute the technical strategy and multi-year roadmap for AI safety engineering and evaluation, aligning priorities with broader organizational objectives. Build, scale, and mentor an emerging multidisciplinary organization of engineering managers, software engineers, machine learning engineers, and applied scientists while owning staffing, resource planning, performance management, and delivery commitments. Provide hands-on technical direction across safety-specific modeling and automated assessments, establishing rigorous engineering standards, review practices, and operating mechanisms that improve execution, maintainability, and organizational effectiveness as the team matures.
- AI/ML Evaluation & Safety Assessment: Lead the development of automated, model-based evaluation approaches (including LLM-as-a-judge, multi-model assessment, and automated classification) to assess models and product experiences for harmful content, bias, robustness, and Responsible AI risks. Establish scientifically rigorous evaluation methodologies, benchmarks, and experimental metrics to quantitatively measure model behavior and mitigation effectiveness. Advance synthetic data generation, scenario-based testing, and automated red teaming to identify systematic failure patterns and characterize emerging safety risks across diverse user experiences.
- Engineering Execution & Scalable Automation: Direct the implementation of safety evaluation workflows, model-based assessors, automated testing logic, and risk-analysis pipelines. Partner closely with Evaluation Systems & Research (ESR) to integrate domain-specific safety capabilities into shared evaluation platforms, transitioning manual expert reviews into repeatable, automated processes embedded in model development, experimentation, and release workflows. Establish robust practices for evaluation data management, benchmark quality, test coverage, and continuous measurement, while managing technical dependencies and delivery priorities across partner engineering teams.
- Cross-Functional Leadership & Influence: Partner with AI/ML engineering, research, product, privacy, legal, and safety policy teams to translate high-level 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 efficacy. Communicate technical strategy, evaluation results, and emerging risks to senior engineering, product, and executive stakeholders, establishing cross-functional operating models that embed automated safety assessments into engineering workflows and reduce reliance on manual expert reviews.
- Minimum Qualifications
- 12+ years of experience in software engineering, machine learning engineering, applied AI, or related technical disciplines, including demonstrated experience delivering complex production-