AIML Resident – ML Research – Controllable Generative AI Full-time

Summary
Imagine what you could do here! At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Combining groundbreaking machine learning research with next-generation hardware, our teams take user experiences to the next level.

Description
Apple’s AIML residency is a year-long program inviting experts in various fields to apply their own domain expertise to innovate and build revolutionary ML and AI-based products and experiences. As AI-based solutions spread across fields, the need for domain experts to understand machine learning and apply their expertise in ML setting grows.

Residents will have the opportunity to attend ML and AI courses, learn from an Apple mentor closely involved in their program, collaborate with fellow residents, gain hands-on experience working on high-impact projects, publish in premier academic conferences, and partner with Apple teams across hardware, software, and services.

Our team is part of the Apple Machine Learning Research (MLR) organization and conducts fundamental research in two areas: (i) controllable generation, aimed at enhancing the controllability and capabilities of multi-modal generative models including 3D, vision, language, audio and motion. This enables techniques such as creating and augmenting virtual worlds with advanced controls, and generating synthetic data for training downstream models; and (ii) efficient machine learning, focusing on optimizing models and algorithms for high performance while minimizing resource usage by improving data efficiency, reducing training time, and lowering inference compute and memory demands. A few recent representative works from the team include:

– “Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks, Methods, and Applications (https://arxiv.org/abs/2311.18168)”, CVPR 2024

– “HUGS: Human Gaussian Splats (https://machinelearning.apple.com/research/hugs)”, CVPR 2024

– “Pointersect: Neural Rendering with Cloud-Ray Intersection (https://arxiv.org/abs/2304.12390)”, CVPR 2023

– “MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training (https://arxiv.org/abs/2311.17049)”, CVPR 2024

– “Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models (https://arxiv.org/abs/2309.10707)”, ICASSP 2024

– “Tic-clip: Continual training of clip models (https://machinelearning.apple.com/research/tic-clip-v2)”, ICLR 2024

– “GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models (https://machinelearning.apple.com/research/gsm-symbolic)”, arXiv 2024

Play a part in building the next revolution of machine learning technology. Our ML Research team is looking for a hard-working resident to conduct research into multi-modal (vision, 3D, language, audio) generative models along with exploring effective control mechanisms for these models. As a member of our team, you will work on some of the most ambitious technical problems, collaborate with world-class machine learning engineers and researchers, develop new ML solutions that will impact future Apple products, and publish your results in high-quality scientific venues.