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About This Role
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.
Preferred Qualifications
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.
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 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, with 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.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $237,600 and $401,700, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Salary Context
This $237K-$401K range is above the 75th percentile for AI Safety roles in our dataset (median: $228K across 14 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 11,520 AI roles we're tracking, AI Safety positions make up 1% of the market. At Apple, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Safety roles pay a median of $266,050 based on 70 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($319K) sits 20% above the category median. Disclosed range: $237K to $401K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Engineering Manager ($253,461) and Research Engineer ($252,000). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Apple AI Hiring
Apple has 38 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Safety, AI Product Manager. Positions span Cupertino, CA, US, Sunnyvale, CA, US, Zurich, Zurich, Switzerland. Compensation range: $194K - $401K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI Safety roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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