Apple

Machine Learning Engineer / Data Scientist

Machine Learning Deep Learning

July 10, 2022

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San Diego, CA, United States

July 10, 2022

Apply Now

Job description

Summary

Posted: Apr 8, 2022

Role Number: 200366170

We are looking for a Machine learning (ML) engineer with broad experience and knowledge in machine/deep learning and computer vision. The Engineer will be doing research on cutting edge problems in data efficient ML, specifically applied to large scale data labeling. The candidate should be proficient in the theoretical fundamentals of the above areas with an interest on making the research work on large ML projects. Topics of interest include ML in the loop data annotation, self-supervised learning, active learning, auto-labeling, among others. As a Machine Learning Engineer / Data Scientist within the Ground Truth Systems Team, you will be part of a team building infrastructure and tools for exciting new technologies that will shape the future of ML. You will have the opportunity to interact with Ground Truth operations and ML Infrastructure team to test your ideas and apply them to create the future of applied ML processes.

Key Qualifications

Strong coding skills in Python using scientific libraries like numpy, scipy.
Experience with one or more deep learning frameworks such as PyTorch, Tensorflow, or Keras is a must.
Experience with training deep neural networks on large-scale datasets.
Understanding of data structures, software design principles and algorithms.
Interest in building machine learning models that assist humans in reducing labeling efforts.
Deep knowledge of traditional ML concepts such as GMMs, SVMs, trees, and boosting as well as more recent deep learning fundamentals.
Previous publication experience in conferences such as CVPR, ICCV, NeurIPS, and ICLR will be strongly considered

Description

The Video Computer Vision org is a centralized applied research and engineering organization responsible for developing real-time on-device Computer Vision and Machine Perception technologies across Apple products. We balance research and product to deliver Apple quality, state-of-the-art experiences, innovating through the full stack, and partnering with HW, SW and ML teams to influence the sensor and silicon roadmap that brings our vision to life. Examples include FaceID, Animoji/Memoji, Scene Understanding, People Understanding and Positional Tracking (VIO/SLAM).