8 hours ago
Responsibilities
- Design, build, and deploy recommendation models that personalize the App Store.
- Develop novel recommendation solutions across retrieval, ranking, multi-task learning, sequence, transformer-based, generative, and multi-objective architectures.
- Ship recommendation models reliably to production at the scale of billions of users.
- Partner with researchers to deploy cutting-edge AI models across Apple's global services.
- Use machine learning engineering and research tools to accelerate development workflows.
Requirements
- Bachelor's and Master's degrees in a quantitative field such as Computer Science, Mathematics, Statistics, or Physics.
- At least 3 years of relevant work experience.
- Hands-on experience with production-level recommender systems.
- Deep knowledge of recommendation systems, design patterns, tools, deep-learning architectures, and multi-task modeling.
- A proven track record of shipping recommendation models to production at scale.
- Knowledge of retrieval, ranking, multi-task learning, sequence and transformer-based models, generative recommenders, and multi-objective optimization.
- Proficiency with the open-source Python ML/AI technology stack, including TensorFlow, PyTorch, scikit-learn, and NumPy/SciPy/Pandas.
- Familiarity with big data technologies and distributed computing, such as Spark, Hadoop, and Kafka.
- Familiarity with LLM-powered coding and research assistants.
- Strong written and oral communication skills.
- PhD in a quantitative field such as Computer Science, Mathematics, Statistics, or Physics is preferred.
Tech Stack
Categories
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.