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About This Role
We’re looking for a Senior Machine Learning Engineer to help build the next generation of tools and platforms that power high\-impact decisions across Siri. In this role, you'll operate at the intersection of engineering excellence and business impact. You’ll design and implement scalable, reliable systems to transform raw data into actionable insights for leadership. This is a high\-visibility, high\-impact position with the opportunity to influence the direction of products and strategy
The Siri User Experience Metrics team is at the heart of shaping how users interact with Siri every day. We use data, metrics and insights to continuously improve Siri’s User Experience across Apple platforms including iOS, macOS, visionOS, tvOS and watchOS.
Our team defines and owns the most critical user facing metrics, builds scalable reporting tools and delivers actionable insights that directly inform product decisions. We collaborate closely with product, platform and feature teams to ensure Siri not only works \- but delivers exceptional User Experience. From response time to failure tracking, we make sure Siri feels fast, natural and helpful wherever users need it.
As a Senior ML engineer on the Siri User Experience Metrics team, you will have significant influence and responsibility in improving Siri user experience by contributing to efforts including building a trustworthy and explainable anomaly detection system to automatically identify regressions in Siri performance and alert engineering teams with actionable insights for debugging performance issues.
If this sounds like you, you're someone who’s laser\-focused on impact \- bringing sharp programming skills, strong problem\-solving abilities and clear communication to the table, all driven by a passion for building exceptional products.
You'll have the opportunity to drive meaningful impact across all Apple platforms by collaborating closely with Engineering, Product, Testing and Quality teams. Your work will directly enhance the Siri experience for billions of users \- shaping how people interact with Apple every day.
Description
We’re looking for a Senior Machine Learning engineer with a proven record of building scalable statistical systems for business applications in a fast\-paced environment. In this role you will drive the technical vision for Siri’s automated anomaly detection platform for detecting performance and reliability regressions.
You are someone who is passionate about shipping quality code and continually improving our anomaly detection systems. You will be responsible for defining, developing and delivering key features for high quality alerting to enable teams to troubleshoot regressions rapidly.
You are someone who works extremely well across teams and organizations and demonstrates strong communication and technical leadership skills and the ability to engage with colleagues and leadership to find common ground on solving hard problems. You are someone who shares technical vision to leadership and engineering teams, gathers feature requirements, defines technical roadmaps and executes efficiently.
You will be responsible for technically representing the team and communicating progress on key deliverables across the organization from peer groups to senior leadership. As the Senior ML engineer on the team, you will be responsible for owning the technical roadmap, onboarding and mentoring team members, and leading the team to deliver high\-impact outcomes.
You are someone comfortable executing in a rapidly changing environment with ambiguous requirements to drive impact incrementally. You demonstrate strong problem solving skills and are self\-directed with a proven ability to execute. You continually desire learning and demonstrate attention to details and find opportunities to innovate and share knowledge with others.
Preferred Qualifications
Proven expertise with anomaly detection and time series modeling (e.g., Isolation Forest, autoencoders, ARIMA, LSTM) and experience building production frameworks supporting multiple engineering and product teams.
Experience with LLM workflows (domain adaptation, RAG) and deploying optimized ML/LLM models on mobile or server environments (e.g., Core ML, TensorFlow Lite, ONNX Runtime) for performance, cost, and privacy.
Experience in developing ML infrastructure, and large\-scale operations, including model serving, distributed training, CI/CD for ML pipelines, and platform monitoring across millions of devices or events.
Familiarity with composite metrics and interpretability tools (e.g., SHAP, LIME), with a track record of publications, patents, or open\-source contributions in ML/LLMs, anomaly detection, or time series modeling.
Minimum Qualifications
Master’s degree with 8\+ years of industry experience in machine learning, or Ph.D. with 5\+ years, applying ML to real\-world business problems.
Strong understanding of core ML concepts, with particular depth in unsupervised learning methods (clustering, dimensionality reduction, density estimation), and a solid foundation in feature engineering, model evaluation, regularization, and optimization.
Advanced coding skills in Python (5\+ years) with pandas, scikit\-learn, and at least one deep learning framework (PyTorch or TensorFlow).
Hands\-on experience data preprocessing, building and training ML models using distributed processing frameworks such as PySpark, Spark, or Flink.
Experience applying large language models (LLMs) for downstream tasks (classification, labeling, enrichment), with the ability to perform fine\-tuning or parameter\-efficient adaptation (e.g., LoRA). Must be capable of deploying and optimizing models in on\-premise, server, or on\-device environments, rather than relying solely on hosted third\-party APIs
Demonstrated ability to set technical vision, lead complex projects, and drive impact in cross\-functional environments, with strong communication and problem\-solving skills.
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 $181,100 and $318,400, 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 $181K-$318K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $184K across 1486 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 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $175,000 based on 11,128 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,500. This role's midpoint ($249K) sits 43% above the category median. Disclosed range: $181K to $318K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. 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: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Apple AI Hiring
Apple has 102 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, MLOps Engineer. Positions span Austin, TX, US, Sunnyvale, CA, US, Cupertino, CA, US. Compensation range: $207K - $487K.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 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 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 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 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). 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 (98) are outnumbered by mid-level (1,283) and senior (1,092) 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 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 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 $252,000, 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 (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 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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