AIML Resident - Efficient ML Research
Job Description
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 machine learning and AI-based products and experiences. As AI-based solutions spread across disciplines, the need for domain experts to understand machine learning and apply their expertise in ML settings 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 Apple's Machine Learning Research organization. We conduct fundamental research in two areas: 1) 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; and 2) controllable generation, sought at improving the controllability and capabilities of multi-modal generative models. This enables techniques such as creating and augmenting virtual worlds with sophisticated controls, and generating synthetic data for training downstream models. A few recent representative research works from our team include:
- “GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models (https://machinelearning.apple.com/research/gsm-symbolic)”, arXiv 2024
- “Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks, Methods, and Applications (https://arxiv.org/abs/2311.18168)”, CVPR 2024
- “MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training (https://arxiv.org/abs/2311.17049)”, CVPR 2024
- “HUGS: Human Gaussian Splats (https://machinelearning.apple.com/research/hugs)”, CVPR 2024
- “Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models (https://arxiv.org/abs/2309.10707)”, ICASSP 2024
- “Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models (https://arxiv.org/abs/2311.18237)”, ICML 2024
- “Tic-clip: Continual training of clip models (https://machinelearning.apple.com/research/tic-clip-v2)”, ICLR 2024
Our Resident will focus on research at the intersection of multi-modal agents (vision, language, audio), foundation models and data-centric learning methods.
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 Apple's Machine Learning Research organization. We conduct fundamental research in two areas: 1) 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; and 2) controllable generation, sought at improving the controllability and capabilities of multi-modal generative models. This enables techniques such as creating and augmenting virtual worlds with sophisticated controls, and generating synthetic data for training downstream models. A few recent representative research works from our team include:
- “GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models (https://machinelearning.apple.com/research/gsm-symbolic)”, arXiv 2024
- “Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks, Methods, and Applications (https://arxiv.org/abs/2311.18168)”, CVPR 2024
- “MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training (https://arxiv.org/abs/2311.17049)”, CVPR 2024
- “HUGS: Human Gaussian Splats (https://machinelearning.apple.com/research/hugs)”, CVPR 2024
- “Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models (https://arxiv.org/abs/2309.10707)”, ICASSP 2024
- “Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models (https://arxiv.org/abs/2311.18237)”, ICML 2024
- “Tic-clip: Continual training of clip models (https://machinelearning.apple.com/research/tic-clip-v2)”, ICLR 2024
Our Resident will focus on research at the intersection of multi-modal agents (vision, language, audio), foundation models and data-centric learning methods.
Minimum Qualifications
- Proficiency in a programming language such as Python, Objective-C, C++, Swift, and/or R. Candidates should have experience completing a moderate-sized software project
- Demonstrated ability to connect and collaborate with others
- Graduate degree in a STEM field or equivalent industry experience in software engineering
- Apple’s AIML residency is a one-year program starting in July 2025. Residents must be available to work full-time (40 hours/week) July 14, 2025 - July 10, 2026
Preferred Qualifications
- Ideal candidates will also have academic, research, or industry experience in at least one of the following: Large Language Models / Generative AI, Machine Learning & ML Research, Natural Language Processing/Speech/Conversational AI, Robotics, or Computer Vision. Thesis, capstone project, internship, co-op, and/or proven experience in these fields qualifies
- We encourage you to submit a cover letter as part of your application explaining: What are your research interests? How do they apply to the specific Residency position you applied to? Why would you would like to join the AIML residency program broadly?
- APPLICATION DEADLINE: DECEMBER 16, 2024
