Machine Learning Engineer

Build recommendation intelligence that powers App Store personalization at global scale at Apple. This onsite role in Seattle focuses on designing, training, and shipping recommendation models that improve relevance and business metrics across key App Store surfaces, with end-to-end ownership from offline evaluation through production monitoring and experimentation.

You will work closely with ML researchers, data engineering, and infrastructure partners to productionize new modeling approaches with high reliability and low latency. The team’s focus is translating state-of-the-art ranking research into production-quality systems, while continually improving both user experiences and measurable outcomes.

What you’ll do

• Design, train, and launch recommendation models that improve relevance and business metrics on core App Store surfaces.
• Own ranker modules end-to-end, including candidate representation, feature engineering, model architecture, offline evaluation, A/B experimentation, and production monitoring.
• Move state-of-the-art ranking techniques from research prototypes into production deployments.
• Partner with ML researchers, data engineers, and infrastructure teams to operationalize new modeling approaches with high reliability and low latency.
• Ship production-quality code while driving engineering best practices, system architecture, and code quality.
• Contribute to the future growth of personalization across Apple’s services ecosystem.

Requirements

• PhD in a quantitative field (including Computer Science, Mathematics, Statistics, Physics, etc.).
• Bachelor’s and Master’s in a quantitative field (including 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, including design patterns and tools, with particular depth in deep-learning architectures and multi-task modeling.
• Proven record of shipping recommendation models to production at scale, with strong understanding of serving constraints for billions of users.
• Knowledge across modern recommendation architectures, including retrieval and ranking, multi-task learning, sequence and transformer-based models, generative recommenders, and multi-objective optimization.
• Strong familiarity with the open-source Python ML/AI stack, including TensorFlow, PyTorch, scikit-learn, numpy-scipy-pandas.
• Familiarity with big data and distributed computing such as Spark, Hadoop, Kafka.
• Familiarity with using LLM-powered tools (coding and research assistants) to accelerate day-to-day engineering and research workflows.
• Strong written and oral communication skills.

Technologies you’ll work with

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

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Range of discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Discretionary bonuses or commission payments, as well as 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 if voluntarily participating in the Employee Stock Purchase Plan
• Learn more about Apple Benefits

Pay & compensation

The base pay range for this role is $142,300 to $263,300 per year, and base pay depends on skills, qualifications, experience, and location.

Apple employees are eligible for discretionary restricted stock unit awards and may purchase Apple stock at a discount through the Employee Stock Purchase Plan. This role might be eligible for discretionary bonuses or commission payments and relocation. Benefit, compensation, and employee stock programs are subject to eligibility requirements and the terms of applicable plans or programs.