Design and ship recommendation systems that drive App Store personalization at global scale.

Design, train, and launch recommendation models to improve relevance and business metrics across key App Store surfaces
Own ranker modules end-to-end, including candidate representation, feature engineering, model architecture, offline evaluation, A/B experimentation, and production monitoring
Bring state-of-the-art ranking methods from research prototypes into production
Partner with ML researchers, data engineering, and infrastructure teams to productionize new modeling approaches with high reliability and low latency
Ship production-quality code and contribute to engineering best practices, system architecture, and code quality
Support long-term growth of personalization across Apple’s services ecosystem

PhD in a quantitative field (e.g., Computer Science, Mathematics, Statistics, Physics, etc.)
Bachelor’s and Master’s in a quantitative field (e.g., Computer Science, Mathematics, Statistics, Physics, etc.)
3+ years of relevant work experience
Hands-on experience building recommender systems in production
Deep knowledge of recommendation systems, design patterns, and tools, with focus on deep-learning architectures and multi-task modeling
Proven ability to ship recommendation models to production at scale, with understanding of serving constraints for billions of users
Knowledge of modern recommendation architectures across retrieval and ranking, multi-task learning, sequence and transformer-based models, generative recommenders, and multi-objective optimization
Proven familiarity with the open-source Python ML/AI stack: TensorFlow, PyTorch, scikit-learn, numpy-scipy-pandas
Familiarity with big data technologies and distributed computing (e.g., Spark, Hadoop, Kafka)
Familiarity with using LLM-powered tools (coding and research assistants) to accelerate engineering and research workflows
Strong written and oral communication skills

Comprehensive medical and dental coverage
Retirement benefits
Discounted products and free services
Reimbursement for certain educational expenses, including tuition
Discretionary bonuses or commission payments and relocation (if eligible)
Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
Eligible for discretionary restricted stock unit awards
Can purchase Apple stock at a discount via voluntary participation in Apple’s Employee Stock Purchase Plan
Learn more about Apple Benefits

Base pay range: $142,300 to $263,300 per year
Base pay depends on skills, qualifications, experience, and location
Discretionary restricted stock unit awards and eligibility to purchase Apple stock at a discount if voluntarily participating in the Employee Stock Purchase Plan
This role might be eligible for discretionary bonuses or commission payments and relocation
Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and plan or program terms

Location: Seattle, WA (onsite)

Machine Learning Engineer

Job Description