Machine Learning and Artificial Intelligence PhD Internships

At Apple you can take part ML internships and be a part of the ongoing revolution that machine learning plays in daily life. Apple’s fully-integrated hardware and software provide unique opportunities to deliver amazing experiences, all while prioritizing user privacy. We do work in all fields of Machine Learning, including, but not limited to, large language models, diffusion models and reinforcement learning, as well as other related areas such as accessibility, privacy, and fairness. As an AIML intern, you will get to explore new methods, apply machine learning to solve ambitious problems, advance state-of-the art technology through research and publications, challenge existing metrics or protocols, and develop new theories that will impact the way we understand machine learning and the experiences it can enable. Come collaborate with some of the best in the business and help build the world's most innovative products and experiences.

Description

In an AIML internship, you will collaborate with researchers, engineers, and program/project managers to tackle innovative challenges. You will also receive technical mentorship and guidance that allows you to learn new things every day, gain practical skills, build real world experience, develop a greater understanding of our industry, and form valuable connections.

Together, you and your team will partner to design and implement an innovative solution for a Machine Learning problem that is meaningful. At the end of your internship, you will have the opportunity to meet and present your work to leadership.

Where appropriate, you will have the opportunity to submit your work for publication at a suitable conference.

Minimum Qualifications

Pursuing a doctoral (PhD) degree in Computer Science, Computer Engineering, Data Science, Applied Mathematics, or related field

At the end of the internship, you must return to school to continue your education or the internship must be the last requirement for you to graduate.

Preferred Qualifications

Proficiency with an object-oriented programming language, such as Python, Swift, Objective C or Java
Experience with ML libraries, such as TensorFlow, PyTorch, CoreFlow, and Sklearn
Practical knowledge related to building and adapting algorithms for machine learning, speech, multimodal sensing, and related areas
Familiarity with crafting, prototyping, and evaluating interactive systems
Excellent mathematical skills in linear algebra and statistics
Demonstrated expertise in at least one of the areas: statistics, econometrics, operations research, quantitative marketing, causal inference, time series analysis, stochastic modeling, optimization and decision making theory
Ability to collaborate with others
Problem solving skills
Applied ML engineering internships: Experience with integrating research prototypes into production applications. Proficiency conducting ethnographic or other situated studies of human interaction with or through interactive technologies. Experience crafting, conducting, analyzing, and interpreting experiments and investigations
Research-focused internships: Research experience in machine learning and a demonstrable record of publishing academic research in peer-reviewed venues