Internship - AI Research

Key Skills

Aartificial intelligence, Java Programming, Machine learning techniques, NeurIPS, Objective-C, Python Programming, PyTorch, Swift, TensorFlow

Job Description

You will 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. You will join an extraordinary team, including world-class software engineers and leading machine learning researchers who are all passionate about applying groundbreaking techniques to AI. We cover 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. If you think your expertise can be useful to Apple, it probably is! Interns collaborate with researchers, engineers, and program/project managers to tackle innovative challenges. 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 AIML leadership. Where appropriate, you will have the opportunity to submit your work for publication at a suitable conference.

Minimum Qualifications

• Research Focused internships
• Research experience in Machine Learning and a demonstrable record of publishing academic research in peer-reviewed venues in relevant conferences (NeurIPS, CVPR, ACL, ICLR, ICCV, EMNLP, etc.)
• Graduate or PhD degree paths preferred.

Preferred Qualifications

• Each AIML team looks for different skill sets. A subset of the following qualifications is desired:
• Knowledgeable with an object-oriented programming language, such as Python, Swift, Objective C or Java.
• Hands-on experience with ML libraries like TensorFlow, PyTorch, CoreFlow, Sklearn, etc.
• Familiar with crafting, prototyping, and evaluating interactive systems.
• Practical knowledge of building and adapting algorithms for machine learning, speech, multimodal sensing, or related areas.
• Experience with integrating research prototypes into production applications.