苹果Computer Vision/Machine Learning Engineer (Low level CV)
任职要求
Minimum Qualifications • M.S. or PhD in Electrical Engineering/Computer Science or a related field (mathematics, physics or computer engineering), with a focus on computer vision and/or machine learning • Rich experiences in video machine learning covering one of the topics: Low level object detection and segmentation / ML-based ISP / Multi-sensory fusion / Multi-modal fusion in ISP • Proven prototyping skills and proficient in coding (C, C++, Python) • Excellent written and verbal communications skills, be comfortable presenting research to large audiences, and have the ability to work hands-on in multi-functional teams Preferred Qualifications • Publication record in relevant venues (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, SIGGRAPH) • Industry experiences with multi-modal foundation model and frameworks • Knowledge and understanding of generative AI, multi-modal large language model, video caption • Solid understanding of state-of-the-arts in Video Understanding and familiar with the challenges of developing algorithms that run efficiently on resource constrained platforms • Team oriented, result oriented, and self motivated
工作职责
The computer vision algorithm engineer will work in a dynamic team as part of the Video Engineering org which develops on-device computer vision and machine perception technologies across Apple’s products. We balance research and product to deliver the highest quality, state-of-the-art experiences, innovating through the full stack, and partnering with cross-functional teams to influence what brings our vision to life and into customers hands. Keywords: Machine learning based ISP; Low level object detection and segmentation; Multiple sensor fusion
The computer vision algorithm engineer will work in a dynamic team as part of the Video Computer Vision org. which develops on-device computer vision and machine perception technologies across Apple’s products. We balance research and product to deliver the highest quality, state-of-the-art experiences, innovating through the full stack, and partnering with cross-functional teams to influence what brings our vision to life and into customers hands.
The computer vision algorithm engineer will work in a dynamic team as part of the Video Engineering org which develops on-device computer vision and machine perception technologies across Apple’s products. We balance research and product to deliver the highest quality, state-of-the-art experiences, innovating through the full stack, and partnering with cross-functional teams to influence what brings our vision to life and into customers hands.
As a Machine Learning Engineer, you will be entrusted with the critical role of innovating and applying state-of-the-art research in foundation models to tackle complex problems. The solutions you develop will significantly impact future Apple products and the broader ML development ecosystem. You will work with a multidisciplinary team to actively participate in the data-model co-design and co-development practice. Your responsibilities will extend to the design and development of data processing pipeline, modeling methodology and effective evaluation metrics. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues.
As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying innovative research in foundation models to with a particular focus on audio data. This includes working across the full ML pipeline—from pre-training on large-scale unlabeled audio corpora to post-training evaluation and fine-tuning with task-specific datasets. The solutions you develop will have a significant impact on future Apple software and hardware products, as well as the broader ML ecosystem. Your responsibilities will extend to designing and developing a comprehensive multi-modal data generation and curation framework for foundation models at Apple. You will also contribute to building robust model evaluation pipelines that support continuous improvement and performance assessment. In addition, the role involves analyzing multi-modal data to better understand its influence on model behavior and outcomes. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. YOUR WORK MAY SPAN VARIOUS APPLICATIONS, INCLUDING: Designing self-supervised and semi-supervised representation learning pipelines, and fine-tuning strategies for tasks like speech recognition and speaker identification. Applying data selection techniques such as novelty detection and active learning across multi modalities to improve data efficiency and reduce distributional gaps. Modeling data distributions using ML/statistical methods to uncover patterns, reduce redundancy, and handle out-of-distribution challenges. Rapidly learning new methods and domains as needed, and guiding product teams in selecting effective ML solutions.