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Making robots behave intelligently
Senior Staff ML Engineer experienced in building production machine learning systems, from first prototype to hundreds of deployed robotic units processing over a billion items. Deep expertise in computer vision, deep learning, and end-to-end MLOps, with a track record of translating research into measurable operational impact across robotics, healthcare, fraud detection, and neuroscience. Drives technical strategy, cross-team adoption, and mentorship. believes in falling in love with the problem, but enjoys when the solution is cutting-edge research.
The Hebrew University of Jerusalem
PhD, Computational Neuroscience
January 1, 2000 – January 1, 2008
Technion - Israel Institute of Technology
Bachelor's degree, Computer Science
January 1, 1996 – January 1, 2000
Ocado Technology
Sr Staff Machine Learning Researcher
November 1, 2022 – May 1, 2026
Toronto, Ontario, Canada · Hybrid
Kindred.ai
Staff Machine Learning Researcher
September 1, 2017 – October 1, 2022
Toronto, Ontario, Canada · Hybrid
IBM
Research staff member at IBM Research, machine learning for healthcare group
January 1, 2012 – July 1, 2017
IBM Research Lab, Haifa
IBM
Research Staff Member at IBM Research, machine learning and data mining group
April 1, 2008 – January 1, 2011
Haifa, Haifa District, Israel
Hebrew University
Teaching Assistant
January 1, 2001 – January 1, 2006
Elbit Systems
Software Developer
January 1, 1997 – January 1, 2002
MLOps (Machine Learning Operations) Fundamentals
CG
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse background spanning academic research (PhD, IBM Research) and industry (Kindred.ai, Ocado Technology), with a consistent focus on machine learning and its applications. Their experience in robotics, healthcare, and fraud detection showcases adaptability and a broad interest in applying ML to various domains. The long tenure at IBM Research and subsequent senior roles in ML suggest a commitment to deep technical work and innovation. The target role of ML Engineer aligns well with their career trajectory and demonstrated skills.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving abilities, communication skills, and leadership through mentoring and driving adoption of new tooling. Their experience in cross-functional collaboration (e.g., with hardware teams) suggests good operational fit. The descriptions indicate a proactive approach to improving systems and processes.