AIML Research Lead (Machine Learning), MLR

Play a part in building the next revolution of machine learning technology. Were looking for a passionate researchers with team leadership experience to work on ambitious curiosity-driven long-term research projects that will impact the future of Apple and our this role youll have the opportunity to work on innovative foundational research in machine learning focusing on LLMs and generative models. As a leader of and active participant in the team you will be inspired by a diversity of challenging problems collaborate with world-class machine learning engineers and researchers and publish your results in high-quality scientific venues.

You have a strong research background in machine learning or related fields and regularly publish your results in the main relevant conferences and make sure that your research results are of high quality and reproducible. You will define your research plan to advance our understanding of machine learning and execute it through implementation and experimentation in collaboration with your colleagues. You have experience mentoring researchers and the skills to guide complex research projects to meaningful conclusions including preparing technical reports for publication and delivering conference talks. You will have the opportunity to collaborate with broader teams across Apple.

PhD or equivalent practical experience in Computer Science or related technical field Demonstrated expertise in machine learning to formulate a research problem design experiment implement and communicate record in relevant conferences (e.g. NeurIPS ICML ICLR AISTATS CVPR ICCV ECCV ACL EMNLP etc).nProven experience leading research teams and mentoring or managing researchers.n

Hands-on experience working with deep learning toolkits such as JAX PyTorch or MLXnStrong mathematical skills in differential calculus probability coding skills as exemplified by e.g. OSS contributions and ability to maintain a coherent and evolving to work as a team player in a diverse collaborative have proposed through previous publications impactful methods in areas of interest to the group such as generative modeling (flow matching diffusion etc.) LLM/VLM training/fine-tuning/inference neural network theory or scaling laws.