PhD Intern, Apple Ads (Machine Learning)

PhD Intern, Apple Ads (Machine Learning)

Summary

People at Apple don’t just build products — they craft the kind of experience that has revolutionised entire industries. The diverse collection of our people and their ideas encourages innovation in everything we do. Imagine what you could do here! Join Apple, and help us leave the world better than we found it. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Every single day, people do amazing things at Apple!

Imagine what you could do here. At Apple, extraordinary ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Our deep focus on the customer experience extends to Apple Ads, where we bring this same approach to advertising, helping people find exactly what they’re looking for and helping advertisers grow their businesses. Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, Apple Maps and MLS Season Pass. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes—from small app developers to big, global brands. Because when advertising is done right, it benefits everyone!

Description

As a Software Engineering Intern within the Apple Ads team, you will contribute to the design, development, and optimization of our advertising platforms, with a particular focus on leveraging machine learning to enhance ad relevance, forecasting, performance, and user experience. You will work alongside experienced engineers on challenging projects, gaining hands-on experience with large-scale data, machine learning models, and distributed systems. Responsibilities may include developing and deploying machine learning algorithms, analyzing data to identify insights, writing clean and efficient code for production systems, participating in code reviews, debugging issues, and collaborating with cross-functional teams to deliver high-quality, privacy-preserving advertising solutions.

Preferred Qualifications

Ability to work independently and as part of a collaborative team
Strong communication and interpersonal skills, with an ability to articulate technical concepts.
Demonstrated ability to learn new technologies quickly and adapt to evolving project requirements.

Minimum Qualifications

Currently pursuing a PhD in Computer Science, Software Engineering, Machine Learning, Data Science, or a related technical field.
Strong foundational knowledge in data structures, algorithms, and object-oriented programming.
Proficiency in at least one programming language commonly used in machine learning or backend development (e.g., Python, Java, Scala, C++).
Familiarity with version control systems (e.g., Git).
Knowledge of database systems (SQL/NoSQL) and data warehousing concepts.
Excellent problem-solving and analytical skills, with an ability to analyze complex data.
Understanding of fundamental machine learning concepts and statistical methods, with experience using machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Exposure to model optimization techniques such as quantization, tensor parallelism, and inference optimization (e.g., ONNX Runtime, TensorRT, vLLM).
Familiarity with modern neural network architectures (e.g., Transformers, DNNs, RNNs) and training pipelines.
Awareness of reinforcement learning, explore/exploit strategies, and bandit-based optimization concepts.
Exposure to GenAI and agentic system concepts and their application to machine learning workflows.
Familiarity with large-scale data processing technologies (e.g., Spark, Hadoop, Flink) and distributed systems (e.g., Ray, high-throughput RPC systems) to support scalable inference and data workloads.
Experience with or exposure to A/B testing infrastructure and performance measurement at scale.
Prior coursework or projects related to advertising technology, recommender systems, natural language processing, computer vision, or privacy-preserving ML techniques (e.g., federated learning).
Ability to communicate effectively, both written and verbal, with technical and non-technical multi-functional teams.

Experience Level

Entry Level