Help & Supportcareers@gravityer.com
Computer Vision/Machine Learning expert
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Fields of expertise: • Computer Vision & Image Processing • Machine Learning • Computational Geometry Programming environments: • C++ (Visual Studio) • Python (Pycharm, Jupyter) • Matlab • OpenCV Research interests: • Artificial Intelligence • Human-Machine Interfaces • Medical Devices/Applications
Tel Aviv University
M.Sc., Electrical Engineering
January 1, 2009 – January 1, 2013
Tel Aviv University
B.Sc., Biomedical Engineering
January 1, 2004 – January 1, 2008
Apple
Deep Learning and Computer Vision Engineer
October 1, 2021 – Present
Israel · Hybrid
Intel Corporation
Deep Learning Data Scientist
February 1, 2018 – August 1, 2021
Haifa District, Israel
PicScout
Computer Vision/Machine Learning Researcher
September 1, 2016 – December 1, 2017
Israel
PointGrab
Algorithms Engineer
November 1, 2012 – August 1, 2016
Omek Interactive
Algorithms Developer
August 1, 2011 – November 1, 2012
NESS TSG
Algorithms Developer
June 1, 2010 – July 1, 2011
Tel Aviv
Surgix
R&D (student position)
December 1, 2007 – November 1, 2008
InSightec
Undergraduate final project
November 1, 2007 – October 1, 2008
Or Yehuda
Cultural Fit Analysis
The candidate has a consistent career trajectory in R&D and algorithm development within the computer vision and machine learning domains, working for both established tech giants and smaller innovative companies. This demonstrates adaptability and a continuous pursuit of challenging technical roles. The breadth of projects, from spatial computing to biometric identification and gesture recognition, indicates a versatile and curious individual who can contribute to diverse technical challenges. The long tenure in the field suggests stability and commitment.
Soft Skills & Operational Fit
The candidate's extensive experience in research and development roles, including inventing algorithms and leading transitions (e.g., Caffe to Keras), suggests strong problem-solving, innovation, and leadership potential. The descriptions indicate a hands-on approach to implementation and a collaborative mindset (supporting software engineering teams, participating in patent committees).