Lavi Shpigelman
ML EngineerMaking robots behave intelligently
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Making robots behave intelligently
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
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.