Apple · Zurich, Switzerland
Apple is inviting applications for its Computer Vision and Machine Learning Research Internship focused on Large Language Model Efficiency in Zurich, Switzerland. The opportunity is intended for doctoral researchers who want to advance artificial intelligence research, develop more efficient machine learning methods, and collaborate with experienced research scientists and engineers.
The internship focuses on improving the efficiency of advanced machine learning systems, including large language models and diffusion models. Researchers will have the opportunity to investigate new techniques, implement experimental solutions, analyse results, and contribute to research that could influence the development and deployment of future AI technologies.
This opportunity is particularly relevant to PhD candidates in computer science, engineering, data science, applied mathematics, and related disciplines. Applicants should have strong machine learning research experience, excellent programming skills, and a record of research contributions in recognised academic venues.
The position is based in Zurich, Switzerland. Apple lists a preferred commitment of approximately six months, with an early start preferred. The application deadline is not specified in the announcement, and applicants should check the official vacancy for the current recruitment status before applying.
The Apple Computer Vision and Machine Learning Research Internship offers doctoral researchers an opportunity to work on challenging problems at the intersection of artificial intelligence, computational efficiency, and advanced machine learning systems.
Interns are expected to participate in the research process from the initial investigation of a problem to the development and evaluation of potential solutions. This can include reviewing scientific literature, identifying promising research directions, designing algorithms, implementing prototypes, and conducting experiments on large computing systems.
The role may involve experiments across multiple computing nodes and graphics processing units. Researchers may also analyse experimental results, prepare scientific manuscripts, and contribute to submissions for leading machine learning conferences.
The research environment brings together expertise in computer vision, machine learning, robotics, computer graphics, and related technical fields. This multidisciplinary setting offers an opportunity to learn from researchers and engineers working on complex scientific and engineering challenges.
• Practical experience conducting advanced machine learning research within Apple's research and engineering environment.
• Collaboration with experienced scientists and engineers on complex artificial intelligence problems.
• Opportunities to develop and test new algorithms, models, and computational methods.
• Experience running large scale machine learning experiments and evaluating research results.
• Potential involvement in preparing scientific papers and submitting research findings to leading conferences.
• Professional development in industrial AI research, machine learning engineering, and computational science.
• Exposure to multidisciplinary research involving computer vision, robotics, computer graphics, and deep learning systems.
The announcement does not specify a salary, stipend, relocation allowance, accommodation support, or other financial benefits. Applicants should confirm the employment terms directly through the official vacancy.
The internship focuses on improving the computational efficiency, performance, and practical deployment of advanced machine learning models.
• Large language model efficiency: Investigating methods to improve model training and inference while managing computational requirements.
• Diffusion models: Exploring efficiency improvements for advanced generative models.
• On device machine learning: Studying methods that allow machine learning systems to operate efficiently on local devices.
• Speculative decoding: Exploring techniques that can accelerate language model generation.
• Contextual sparsity: Investigating approaches that reduce unnecessary computation during model execution.
• Model quantisation and compression: Exploring ways to reduce model size and computational demands while preserving useful performance.
• Deep learning architectures and systems: Developing efficient model designs and supporting systems for training and inference.
Researchers may explore ways to reduce computational costs while maintaining model quality, helping make advanced AI systems more efficient and practical.
Applicants should review the official vacancy carefully and ensure that their academic background and technical experience match the requirements.
• Applicants must be working towards a doctoral degree in computer science, engineering, data science, applied mathematics, or an equivalent discipline.
• Candidates should demonstrate substantial research expertise in machine learning.
• Applicants should have research publications or a relevant publication record at recognised machine learning conferences, such as NeurIPS, ICLR, ICML, or COLM.
• Strong software engineering skills are required.
• Applicants should have experience working with complex technical systems and multiple programming languages.
• Fluency in Python is essential.
• Candidates should be able to design, implement, and evaluate machine learning experiments.
• Applicants should demonstrate the ability to collaborate on complex research and engineering projects.
The following experience may strengthen an application.
• Knowledge of machine learning algorithms and established deep learning practices.
• Experience with frameworks such as PyTorch or TensorFlow.
• Strong software development skills and familiarity with reliable engineering practices.
• Ability to write clean, maintainable, and well tested code.
• Experience conducting large scale machine learning experiments and evaluating their outcomes.
• Evidence of research contributions relevant to efficient deep learning, model inference, or computational optimisation.
The announcement does not provide a complete document checklist. Applicants should follow the requirements on Apple's official recruitment portal and prepare materials that clearly demonstrate their suitability.
• An up to date CV highlighting doctoral studies and research experience.
• A list of relevant academic publications.
• Evidence of machine learning research contributions.
• Details of Python programming and software engineering experience.
• Information about experience with deep learning frameworks and large scale experiments.
• Relevant research or technical project experience.
Applicants should ensure that their CV clearly explains their individual contributions to research projects, the methods they developed, the systems they implemented, and the results they achieved.
Candidates are likely to be assessed against the academic and technical requirements stated in the vacancy. A competitive application should demonstrate strong research ability, technical depth, and the capacity to contribute to ongoing machine learning projects.
Important considerations include the relevance of previous research, experience with advanced machine learning models, programming ability, experimental design, and the quality of published work. Applicants should also show that they can communicate research findings clearly and work effectively with a multidisciplinary team.
Zurich, Switzerland
Apple
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