below marketmost similar roles pay hereabove market
This role pays more than
98%
of similar roles. Most pay
$203,850–$254,750
— the blue band above.
At the midpoint, this role pays about
$335k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3544 open roles
on FindRole.
Listed pay typically runs
$166,600–$277,600
across 2734 roles with salary data.
Engineering Leader, AI Safety Engineering & Evaluation, Apple Services Engineering leads a multidisciplinary organization of engineering managers, software engineers, and applied scientists within the Human-Centered AI organization. This role involves developing technical strategies and roadmaps to identify, measure, and mitigate risks in AI-powered experiences across products like the App Store and Apple Music. The leader will build automated, scientifically rigorous processes for safety evaluation, including LLM-as-a-judge, multi-model assessment, adversarial testing, and synthetic data generation. Key responsibilities include establishing engineering practices, managing resource planning, and translating safety requirements into measurable technical standards. The role requires expertise in Python, machine learning architectures, and modern software development. The work focuses on transforming manual safety assessments into scalable, automated workflows to ensure AI models are robust, fair, and representative of diverse communities.
Develop and execute the multi-year technical strategy and roadmap for AI safety engineering and evaluation.
Build and mentor a multidisciplinary organization of engineering managers, software engineers, and applied scientists.
Lead the development of automated, model-based evaluation approaches like LLM-as-a-judge and automated classification.
Establish scientifically rigorous benchmarks and experimental metrics to measure model behavior and mitigation effectiveness.
Direct the implementation of safety evaluation workflows and risk-analysis pipelines into shared evaluation platforms.
Transition manual expert safety reviews into repeatable, automated processes embedded in model development and release workflows.
Translate high-level safety requirements into measurable technical criteria and actionable insights for product teams.
Communicate technical strategy and emerging risks to senior engineering, product, and executive stakeholders.
What we're looking for
12+ years of experience in software engineering, machine learning engineering, applied AI, or related technical disciplines.
8+ years of engineering or applied machine learning management experience, including at least 3 years managing engineering managers.
Demonstrated experience building or transforming engineering organizations, developing technical talent, and establishing engineering practices.
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.
Bachelor's degree in Computer Science, Machine Learning, Engineering, or a related technical field, or equivalent practical experience.
Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical discipline (preferred).
Generative AI
LLMs
AI Evaluation
Responsible AI
Agentic AI
LLM-as-a-Judge
AI Safety
Synthetic Data
Red Teaming
Automation
Benchmark Design
Evaluation Infrastructure
Technical Roadmap
Program Management
Data Engineering
Big Data
Agile
Scrum
Sprints
Data Analytics
Software Development
Automation
Information Security
Privacy Engineering
Quality Assurance
Digital Marketing Technologies
Machine Learning
Generative AI
ML Lifecycle
Data Engineering
SRE
Model Training
Data Quality
Technical Scoping
Requirements Analysis
Technical Design
Product Roadmap