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About This Role
We are seeking a highly motivated and analytical AI Experience Researcher to join our team. This role blends cognitive and human sciences, data sciences, systems design, and product evaluation to ensure AI\-powered products deliver exceptional and intuitive customer experiences.
You will work alongside a small but impactful team, collaborating with ML and data scientists, software engineers, designers, project managers, and other cross\-functional teams at Apple to define success criteria for AI experiences, and create rigorous evaluations that measure these criteria in iterative product development cycles. If you're passionate about applying scientific rigor to real\-world problems, thrive on innovation, and want your work to impact hundreds of millions of users, this role offers an exceptional opportunity to make a lasting contribution to products people use every day.
Description
The central challenge of this role is figuring out what "good" means for an AI experience, and then designing rigorous evaluations that measure those qualities reliably and at scale. This requires both deep theoretical grounding in human experience and a solid analytical mindset to operationalize that understanding into scalable evaluation frameworks.
Leaning on research in human sciences, you will decompose complex AI interactions into their constituent parts, reason about how those parts interact, and build evaluation frameworks that hold up under the scrutiny of non\-deterministic nature of AI experiences and the pressures of iterative product development. You will derive experimental designs, create golden data sets, write tests, and turn them into prompts for LLM judges or instructions for human raters. You will run automated evaluations, analyze results, and present findings to diverse stakeholders.
Candidates who bring both quantitative rigor and a qualitative sensibility \- to recognize patterns in model behaviors and outputs, and to develop an interpretive understanding of what the data is and isn't capturing from a human perspective \- will thrive in this role.What matters most is the ability to hold both orientations at once \- to think carefully about what makes an experience work, and to measure complex human dimensions with precision. We are also looking for someone who is excited to co\-create what this discipline looks like going forward \- bringing intellectual curiosity and a point of view about where human\-centered AI evaluation should be headed.","responsibilities":"Develop scalable automated evaluation methodologies by operationalizing complex multi\-modal multi\-turn AI experiences into observable and measurable metrics that work across diverse use cases, features, or product area
Produce comprehensive evaluation plans detailing evaluation scope, validation and data strategy, tooling requirements, resource allocation, and timelines
Derive experimental designs and write test instructions for LLM judges or for human raters
Define requirements for, or curate datasets that represent realistic usage; support data generation and annotation workflows to ensure coverage, quality, and alignment with product goals
Implement and analyze automated evaluations, maintaining rigor around reproducibility, identifying key insights, and areas for improvement across both qualitative and quantitative patterns
Prepare and present clear, concise, and impactful evaluation findings to diverse stakeholders, translating results into actionable recommendations for model training, ranking, and product decisions
Partner with engineers, QA, data scientists, designers, and product managers throughout the product development lifecycle to integrate evaluation insights and drive continuous improvement
Contribute to evolving human\-centered AI evaluation methodologies and help to define best practices for AI experience evaluation as the field matures
Preferred Qualifications
Familiarity with methods for capturing experiential quality beyond task success \- such as cognitive interviews, think\-aloud protocols, interaction analysis, or discourse and conversation analysis
Experience designing and implementing automated evaluation pipelines, including writing prompts for LLM judges and constructing human\-in\-the\-loop or multi\-turn evaluation setups
Experience working with multimodal or agentic systems, AI/ML models, preferably Large Language Models
Familiarity with automated testing frameworks and tooling
Experience with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage
Portfolio demonstrating previous evaluation frameworks, research findings, or measurable contributions to product improvement
Background in learning sciences or instructional design, with experience reasoning about what makes a complex human experience effective is a plus
Minimum Qualifications
Advanced degree in Cognitive Psychology, Human\-Computer Interaction (HCI), User Experience (UX) Research, Learning Sciences, Learning Analytics, Psychometrics, Applied Behavioral Science, or a related field with a focus on human cognition, behavior, and empirical evaluation
A strong data\-driven mindset with experience designing and conducting rigorous empirical research or evaluation \- including experimental design, data analysis, and interpretation of various qualitative and quantitative data \- particularly in the context of complex human\-system interactions
Ability to reason from theoretical grounding about what makes an experience good in a given context, and to translate that reasoning into evaluation frameworks and measurement designs
Demonstrated ability to operationalize research literature, qualitative user feedback, and quantitative behavioral data into actionable evaluation criteria, observable metrics, and product insights
Proficiency in data analysis and interpretation, with a strong understanding of statistical validity in evaluation contexts
Exceptional collaboration skills with a track record of working effectively in cross\-functional teams that include engineering, ML, design, QA, leadership, and subject matter experts of diverse domains
Strong communication skills, with the ability to translate complex research findings and evaluation results into clear, actionable recommendations for both technical and non\-technical audiences
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .
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 $134,800 and $245,800, 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 $134K-$245K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Apple, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($190K) sits 14% above the category median. Disclosed range: $134K to $245K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Apple AI Hiring
Apple has 160 open AI roles right now. They're hiring across Research Engineer, MLOps Engineer, AI/ML Engineer, AI Software Engineer. Positions span Cupertino, CA, US, Austin, TX, US, Santa Clara, CA, US. Compensation range: $153K - $487K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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