About this role
Summary: Join the Multimodal Intelligence Team to bridge research advances and practical applications in generative AI and multimodal foundation models. You will evaluate emerging research, conduct applied experiments, and collaborate with engineering teams to transform promising approaches into robust solutions for future A...
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Multifaceted, amazing people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same passion for innovation that goes into our products also applies to our practices, strengthening our commitment to leave the world better than we found it. Join us in this truly exciting era of Artificial Intelligence to help deliver the next groundbreaking Apple products & experiences! We are continuously advancing the state of the art in Computer Vision and Machine Learning, touching all aspects of language and multimodal foundation models, from data collection, data curation to modeling, evaluation and deployment. As a member of our dynamic group, you will have the unique and rewarding opportunity to craft upcoming research directions in the field of multimodal foundation models that will inspire future Apple products. You will be working alongside highly accomplished and deeply technical scientists and engineers to develop pioneering solutions for challenging problems. This is a unique opportunity to be part of what forms the future of Apple products that will touch the lives of many people. We (Multimodal Intelligence Team) are looking for an Applied AI Scientist to work on the field of Generative AI and multimodal foundation models. Our team has an established track record of shipping features that leverage multiple sensors, such as FaceID, RoomPlan and hand tracking in VisionPro, as well as a strong research presence in the multimodal AI community. Our publications span multimodal pre-training, vision-language models, video-language models, and multimodal alignment. We are focused on building experiences that demonstrate the power of our sensing hardware as well as large foundation models.
Description
This position requires a highly motivated person who wants to help us bridge the gap between research advances and practical applications in generative AI and multimodal foundation models. You will be responsible for evaluating and adapting emerging research, conducting applied research experiments, and working with engineering teams to transform promising approaches into robust solutions, taking into account future hardware design and product needs. In addition, you will have an opportunity to engage and collaborate with several teams across Apple to deliver the best products.
Minimum qualifications
Experience in deep learning with demonstrated work in at least one area of multimodal systems (e.g. vision, language, video, etc.).
Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX.
Experience with rapid prototyping, reproduction, and validation of research ideas.
Master's or equivalent practical experience, in Computer Science, Computer Vision, Machine Learning, or related technical field.
Preferred qualifications
PhD, or equivalent practical experience, in Computer Science, Machine Learning, or a related technical field.
Demonstrated expertise in deep learning, with either: A publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV,COLM, etc), or a strong track record of applying deep learning techniques to real-world products.
Experience with foundation models (language or multimodal).
Familiarity with large-scale data pipelines, including data curation, preprocessing, and efficient storage.
Experience fine-tuning or optimizing large models for production deployment.
Experience applying foundation models to build autonomous or semi-autonomous agents, including planning, task decomposition, and multi-step reasoning.
Familiarity with privacy-preserving or on-device machine learning.
Ability to work effectively in a multi-functional, collaborative environment.