SUMMARY

Posted: Feb 25, 2025
Weekly Hours: 40
Role Number:200592777
The System Intelligence and Machine Learning (SIML) organization at Apple is looking for a Multi-Modal LLM Research Engineer to help shape the future of on-device Apple Intelligence. In this role, you will work at the intersection of large language models, neural network optimizations, and algorithm development, driving innovations that enhance real-world AI experiences for millions of users.

DESCRIPTION

As part of a collaborative team of deep learning experts and software engineers, you will explore the optimal trade-offs between model quality and efficiency, ensuring that innovative Multi-Modal LLMs can be seamlessly deployed on-device. You will translate the latest research into practical engineering solutions or innovate novel technologies, shaping key decisions on on-device model deployment and real-world performance. Working closely with various teams at Apple, you will help design Multi-Modal LLM architectures, refine training paradigms for real-world applications, and develop software optimized for emerging hardware architectures-potentially even influencing future hardware designs. If you want to be part of a science- and results-driven team and are comfortable embracing new challenges in a fast-paced, iterative environment, we’d love to hear from you. Your research and development will directly shape the next generation of Apple Intelligence experiences!

MINIMUM QUALIFICATIONS

• Masters, or Ph.D. in Computer Science, or Computer Engineering; similarly related fields, or comparable professional experience.
• Experience on developing/optimizing/training large language models (LLMs), or large computer vision models, or generative AI models.
• Proven track record to drive scientific investigations and experiments and overcome obstacles and uncertainty in a research environment.
• Excellent communication and collaboration skills, and have the ability to work hands-on in multi-functional teams.
• Solid mathematical foundation of machine learning and deep learning techniques.
• Strong programming skills in Python, solid understanding of C++.
• Proficiency in at least one deep learning framework (e.g., PyTorch, Keras, TensorFlow, JAX).

PREFERRED QUALIFICATIONS

• Strong background in research and innovation, demonstrated through publications in top-tier journals or conferences, patents, or impactful industry experience.
• Experience with network optimization algorithms, e.g. quantization and compression, sparsification, knowledge distillation, or neural architecture search.
• Deep understanding of computer systems and the interactions between HW and SW.

Please refer the Job description for details