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About This Role
The Apple Services Engineering (ASE) organization is one of the most exciting examples of Apple’s long\-held passion for combining art with technology. We are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books. And we do it on a massive scale, meeting Apple’s high expectations with high performance, to deliver a huge variety of entertainment in over 40 languages to more than 170 countries. Our scientists and engineers build secure, end\-to\-end solutions powered by Artifical Intelligence \& Machine Learning.
Thanks to Apple’s unique integration of hardware, software, and services, designers, scientists and engineers in ASE partner to get behind a single unified vision. That vision always includes a deep commitment to strengthening Apple’s privacy policy, one of Apple’s core values. Although services are a bigger part of Apple’s business than ever before, these teams remain small, flexible, and multi\-functional, offering greater exposure to the array of opportunities here.
We are looking for a Machine Learning Research Engineer to join our mission. You will design and develop the AI/ML solutions that power these experiences, from proposing and prototyping new algorithms to building reusable capabilities for the entire organization. In our flexible and collaborative environment, you'll work with designers and engineers to build secure, end\-to\-end solutions that honor Apple's core value of user privacy.
Description
In this role, you will drive technical advancement, influence product direction, and be part of the team that is responsible for bringing the latest advancements in Machine Learning, Natural Language Processing, as well as Generative and Agentic AI to drive major impact on how users discover Apple Media content on devices worldwide!
Our team has strong expertise in NLP, Information Retrieval, Machine Learning, Language Modeling, Generative AI, Data Mining, and Distributed Computing (Hadoop, Scala, Spark).
You will use data driven analysis to ideate, evaluate and prioritize features, and conduct A/B Tests to ensure we objectively measure improvements. You will ensure successful delivery of features, code, data, and models to production. You will collaborate with researchers, engineers, and operations teams to ensure that features and models are functioning at or above expected performance levels, globally in languages from Arabic to Vietnamese and everything in between!
","responsibilities":"Tackle complex research challenges by simplifying problems, inventing novel solutions, and driving concepts from ideation to production.
Present key technical findings and research contributions to both internal teams and the wider public community.
Design, fine\-tune, and deploy Large Language Models (LLMs) and other advanced ML models for user\-facing features.
Develop and optimize high\-performance components within large\-scale distributed systems, using languages such as C\+\+ and Go.
Own the end\-to\-end deployment process, ensuring that features, code, data, and models are successfully launched and monitored in production.
Perform in\-depth data analysis using big data technologies to identify opportunities for improving content discovery and the overall user experience.
Design, conduct, and analyze rigorous A/B tests to objectively measure the impact of new features and ensure performance meets or exceeds expectations.
Partner with a world\-class team of engineers, researchers, and statisticians to design and deliver cohesive, scalable AI solutions.
Work closely with cross\-functional teams to initiate and successfully complete projects that directly enhance the user experience on services like the App Store.
Directly impact how millions of users discover content across the entire Apple ecosystem, including all devices (macOS, iOS, tvOS, watchOS, visionOS) and services, supporting dozens of languages globally.
Preferred Qualifications
Master's or PhD in Computer Science, Computer Engineering, Information Systems, Electrical Engineering, or a related technical field.
4\+ years of professional AI/ML experience with a track record of shipping production models.
Deep expertise in Search and Information Retrieval, including indexing, query understanding, retrieval models, and ranking algorithms.
Hands\-on experience with modern AI paradigms and tools, such as Retrieval\-Augmented Generation (RAG), agentic workflows, vector databases (e.g., Pinecone, FAISS), and knowledge graphs.
Experience with large\-scale data processing and pipeline construction using technologies like Spark, Hadoop, Java, or Scala.
Expertise in building low\-latency model serving systems, with experience in languages like Go (Golang) is a plus.
Experience providing technical leadership on complex, cross\-functional projects.
Excellent communication skills and a history of collaborating with partners to design and deliver scalable ML solutions.
Minimum Qualifications
Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Electrical Engineering, or a related technical field.
2\+ years of professional experience in the field of Artificial Intelligence/Machine Learning (AI/ML).
Proficiency in Python and experience with ML frameworks such as PyTorch or TensorFlow.
Industry experience in one or more of the following areas: Generative AI, Agentic AI systems, Natural Language Processing (NLP) (e.g., text summarization), Search Relevance and Ranking, Online Advertising, or Recommendation Systems.
Solid understanding of modern machine learning concepts, including Transformers, LLMs, model evaluation techniques, and large\-scale data pipelines.
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 $139,500 and $258,100, 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 $139K-$258K range is above the median for Research Scientist roles in our dataset (median: $196K across 93 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,824 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Apple, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 223 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($198K) sits 11% below the category median. Disclosed range: $139K to $258K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Apple AI Hiring
Apple has 109 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Safety, AI Product Manager. Positions span Cupertino, CA, US, Seattle, WA, US, Austin, TX, US. Compensation range: $207K - $487K.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% above the national median.
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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