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Lead Machine Learning Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
For the last 6 years I was allocating around 60% of my time into Deep Learning computer vision, and 40% of my time in development, deployment (both on cloud, browsers and embedded devices) and data science connected to machine learning. I tend to practice algorithms on https://www.hackerrank.com/Huxwell . Also, I'm a rock climber.
Wrocław University of Science and Technology
Magister inżynier (Mgr inż.), Informatyka / Indywidualny program studiów
January 1, 2015 – January 1, 2017
Wrocław University of Science and Technology
Inżynier, Computer Science
January 1, 2012 – January 1, 2015
GlobalLogic
Lead Machine Learning Engineer / ML Architect
November 1, 2020 – Present
Wrocław, Woj. Dolnośląskie, Polska
Mythic
Deep Learning Researcher
November 1, 2019 – November 1, 2020
Redwood City, Kalifornia, Stany Zjednoczone
NAUTO
Senior Software Developer (automotive Deep Learning)
October 1, 2017 – November 1, 2019
Palo Alto
IDENTT sp. z o.o.
Machine Learning Engineer
April 1, 2016 – September 1, 2017
ZMorph
Android Developer
January 1, 2014 – January 1, 2014
Inwestycje Alternatywne Profit S.A.
Web Developer
January 1, 2012 – January 1, 2012
Warszawa
Cultural Fit Analysis
The candidate's background is heavily focused on Machine Learning Engineering and Deep Learning, with significant experience in automotive, medical, and industrial sectors. While these roles involve data analysis, the primary focus has been on model development and deployment rather than traditional data analysis (e.g., business intelligence, statistical analysis, reporting). This suggests a potential mismatch with a pure 'Data Analyst' role, which typically requires a different skill set emphasizing data manipulation, visualization, and business insights over ML model building. The project diversity is high within the ML domain, but less so in broader data analysis contexts.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership in ML projects, coordination of labeling efforts, and technical interviewing, suggesting strong operational and team collaboration skills. The ability to tackle complex challenges and optimize solutions for specific constraints (speed, memory) indicates a problem-solving mindset.