Job Details

Weekly Hours: 40

Role Number: 200600956-3337

Summary

The Machine Intelligence, Neural Design (MIND) team is leading Apple-wide innovation on HW/SW co-design for efficient inference. We are seeking a highly motivated, results-driven team player with a strong background in machine learning and systems, along with hands-on software development experience. The role focuses on optimizing mobile-cloud hybrid inference for multi-modal large language models (LLMs), with emphasis on both systems-level innovation and ML co-design.

Our mission is to advance the state-of-art in ML optimization techniques for efficient inference. Thus, the ideal candidate for this position is a researcher and engineer who will relentlessly push the existing boundary with out-of-box thinking, and materialize the idea with the very details to impact both Apple partners and research communities.

Description

• Research and develop ML optimization techniques for efficient on-device ML
• Materialize the ideas/concepts and demonstrate the practical value in an industrial environment
• Work with HW/SW teams to productize the outcome of the work

Minimum Qualifications

• Expert in Python/PyTorch programming

• Quality publications in System optimization (e.g., MICRO, ASPOLOS, OSDI, NSDI, MobiSys etc).

• Quality publications in ML optimization (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, etc).

• Excellent communication and writing skills

• Ph.D. in CS/CE or related fields

Preferred Qualifications

• Experience with Large-scale model training/tuning

• Experience with processing multiple modalities (i.e., audio/vision) with Foundational Models.

• Experience with backend compilation, HW/SW co-design, and/or performance optimization

• Understanding ML accelerations and architectures

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .