Apple AR/VR Job | Computer Vision Machine Learning Student – Imaging and Sensing Group

Job(岗位): Computer Vision Machine Learning Student – Imaging and Sensing Group

Citys(岗位城市): Herzliya, Israel

Date(发布日期): 2022-12-21


The Imaging and Sensing group at Apple develops algorithms for scene understanding and depth sensing using active (e.g Lidar and True Depth cameras) and passive (e.g Monocular and Stereo RGB cameras) sensors for enabling features for Photography, AR, and more. This multi-disciplinary group of engineers is responsible for research, architecture, design, and development of future Apple products and technologies.

The Imaging and Sensing group is looking for an highly motivated, extraordinary advanced degree student with major in computer vision or machine learning to drive innovative technologies for Apple products. As part of the team you would work in interdisciplinary team, that combines expertise in image processing, computer vision, deep learning and software engineering. You will collaborate with other teams inside the group and be exposed to variety of projects.

We are looking for a creative and dedicated computer vision machine learning engineer with a strong software and deep learning experience. In this role you will be responsible to develop innovative NN architectures to improve data usage and training results.

As a member of a fast-paced team, you have the unique and rewarding opportunity to shape upcoming products that will delight and inspire millions of people every day.


Academic experience and or thesis in computer vision or machine learning.

Profound knowledge in deep learning techniques combined with practical experience with TensorFlow, PyTorch or similar.

Strong coding skills in Python

Experience with classic computer vision algorithms.

Striving for excellence.

Independent and good collaboration skills.

Creativity and curiosity for solving highly complex problems.

Familiarity with depth sensing technologies – an advantage.


Develop and deliver advanced NN architecture frameworks to improve data usage and training results. This includes understanding complex data and training pipelines and analysis of results using internal tools. Investigate different aspects of the data (real or synthetic). Join forces and work with top teams in software to improve and validate the results and ensure the full pipeline runs efficiently. Cooperating with your team members to prepare presentations, papers, and talks to explain your inventions.