Job description

We are looking for an exceptional Machine Learning Engineer to help us design, build, and ship recommendation models that power personalization across the App Store. With your expertise, we want to develop novel solutions to power personalized experiences across the App Store that enrich the lives of our customers. You will have the incredible opportunity to partner with researchers to see cutting-edge AI models deployed reliably at Apple's truly incredible global scale. 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, design patterns and tools, with particular depth in deep-learning architectures and multi-task modeling. Proven track record of shipping recommendation models to production at scale, with a strong understanding of the constraints of 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. Proven grasp of the open-source Python ML/AI tech stack, including 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 day-to-day engineering and research workflows. Strong written & oral communication skills. PhD in a quantitative field, including Computer Science, Mathematics, Statistics, Physics, etc.

Requirements

Must-haves

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, design patterns and tools, with particular depth in deep-learning architectures and multi-task modeling.
Proven track record of shipping recommendation models to production at scale, with a strong understanding of the constraints of 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.
Proven grasp of the open-source Python ML/AI tech stack, including 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 day-to-day engineering and research workflows.
Strong written & oral communication skills.
PhD in a quantitative field, including Computer Science, Mathematics, Statistics, Physics, etc.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaipythonmlllm

Platform: apple
Effort: medium