Interested in this AI/ML Engineer role at Apple?
Apply Now →Skills & Technologies
About This Role
The Apple Services Engineering AI/ML organization is hiring a Machine Learning Engineer to join the Search team for Video catalog.
Our team builds the core intelligence that powers search discovery experiences in the Apple TV App, Siri, and Spotlight cross platforms, helping users effortlessly find and enjoy the content they love. We are a collaborative, high\-impact team that values innovation, craftsmanship, and end\-to\-end ownership from idea to launch. Our systems combine large\-scale data, modern retrieval and ranking models, and a deep commitment to user privacy.
Join us, you’ll develop scalable systems and machine learning models that drive search relevance, personalization, and understanding of content at scale. Working closely with cross\-functional partners in product and design, you’ll translate cutting\-edge research in advanced machine learning and generative AI into secure and delightful production features used by millions every day.
Description
As a Machine Learning Engineer on the Video Search team, within the Apple Services Engineering AI/ML organization, you will design and deploy large\-scale ML systems that power search and discovery across Apple platforms.
You’ll apply machine learning, natural language understanding, and generative AI to model user intent and deliver relevant, personalized results. Your work will involve building and optimizing cutting edge data processing, ML models, retrieval pipelines, and ranking systems that operate at global scale and under strict privacy standards.
This is a hands\-on role where you will collaborate closely with cross\-functional teams to bring advanced ML technologies into production\-shaping how users discovery content they love in Apple TV app, cross Apple TV partners on Apple Platforms, also through Siri and Spotlight.","responsibilities":"Solve complex research problems and implement solutions from concept to execution.
Design and implement retrieval and ranking systems using semantics and user context.
Build and deploy ML, NLP and LLM models to improve search relevance and personalization.
Analyze data and model performance to identify opportunities for search quality enhancement.
Develop automated tests for continuous integration and ensure successful production deployment.
Conduct A/B tests to measure search improvements.
Collaborate with cross\-functional teams to innovate intelligent video catalog text search.
Enhance search recall and ranking for global Apple devices across all platforms and languages.
Utilize big data tech to evaluate content discovery features.
Ensure systems meet Apple’s privacy, efficiency, and user experience standards.
Preferred Qualifications
Experience with search or recommendation systems, and semantic retrieval or vector databases.
Expertise in transformer architectures, embeddings, and retrieval or ranking models.
Experience in applying or fine\-tuning LLMs for understanding and generation tasks. Familiarity with prompt design, context management, RAG and Agentic architectures and solutions.
Exposure to evaluation and safety frameworks for LLM\-based systems.
Knowledge of reinforcement learning and other modern post training practices for LLMs.
Passion for developing intelligent, human\-centered experiences to enhance content discovery.
Minimum Qualifications
Experience in machine learning, NLP, IR, or more recently Large Language Model ( LLMs).
Strong programming skills in Python, Java and Go for building scalable ML systems.
Hands\-on expertise in ML libraries such as PyTorch, JAX, TensorFlow for model training and deployment.
Ability to translate product goals into technical solutions, improving user experience.
Strong communication, collaboration, and analytical problem\-solving skills.
In\-depth knowledge of search and information retrieval fundamentals, including indexing and ranking. Experience with retrieval and ranking algorithms and building big data pipelines using Hadoop, Java, Scala, Spark and more.
Industrial experience in search, classification, recommendation systems, or related fields.
Familiarity with A/B testing and data\-driven product development.
Passionate about creating products loved by customers at Apple.
Bachelor’s degree or higher (or equivalent practical experience) in Computer Science, Machine Learning, Natural Language Processing, Artificial Intelligence, or a related field.
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 $147,400 and $272,100, 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 $147K-$272K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($209K) sits 13% above the category median. Disclosed range: $147K to $272K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Apple AI Hiring
Apple has 62 open AI roles right now. They're hiring across AI/ML Engineer, LLM Engineer, AI Product Manager, AI Software Engineer. Positions span Cupertino, CA, US, San Diego, CA, US, Seattle, WA, US. Compensation range: $190K - $487K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 tracked positions. That's 26% above the national 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.