Machine Learning Engineer

Work on recommendation systems that power App Store personalization through end-to-end model development and reliable production deployment at global scale.

Responsibilities

• Design, train, and launch recommendation models to improve relevance and business metrics across key App Store surfaces.
• Own ranker modules end-to-end, covering candidate representation, feature engineering, model architecture, offline evaluation, A/B experimentation, and production monitoring.
• Translate state-of-the-art ranking techniques from research prototypes into production systems.
• Partner with ML researchers, data engineers, and infrastructure teams to productionize new modeling approaches with high reliability and low latency.
• Ship production-quality code and help drive engineering best practices, system architecture, and code quality within the team.
• Contribute to planning the continued growth of personalization across Apple’s services ecosystem.

Requirements

• PhD in a quantitative field (Computer Science, Mathematics, Statistics, Physics, etc.).
• Bachelor’s and Master’s in a quantitative field (Computer Science, Mathematics, Statistics, Physics, etc.).
• 3+ years of relevant work experience.
• Hands-on experience with production-level recommender systems.
• Deep knowledge of recommendation systems, design patterns, and tools, with strong depth in deep-learning architectures and multi-task modeling.
• Demonstrated experience shipping recommendation models to production at scale, including understanding constraints involved in serving 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.
• Strong working grasp of the open-source Python ML/AI stack: TensorFlow, PyTorch, scikit-learn, numpy-scipy-pandas.
• Familiarity with big data and distributed computing technologies (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.

Technologies

• Python
• TensorFlow
• PyTorch
• scikit-learn
• numpy-scipy-pandas
• Spark
• Hadoop
• Kafka
• LLM-powered tools

Location

• Seattle, WA (onsite)

Compensation

• Base pay range: USD 142,300 - 263,300 per year
• Base pay depends on skills, qualifications, experience, and location.

Benefits

• 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 discretionary employee stock programs
• Eligible for discretionary restricted stock unit awards
• Can purchase Apple stock at a discount if voluntarily participating in the Employee Stock Purchase Plan
• 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.