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Deep Learning Research and Development Engineer at NVIDIA
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Assessing your cultural and operational fit
-> Ph.D in Electrical Engineering with 7+ years hands-on experience, including 3+ years industry working experience and 4 years academic research experience.
New York University - Polytechnic School of Engineering
Doctor of Philosophy (PhD), Electrical and Electronics Engineering
January 1, 2011 – January 1, 2014
Warsaw University of Technology
Master of Science (M.Sc.), Electronics and Computer Engineering
January 1, 2004 – January 1, 2009
NVIDIA
Deep Learning Research and Development Engineer
December 1, 2015 – Present
HEVO Power
CTO
June 1, 2015 – December 1, 2015
NYU Polytechnic School of Engineering
Post-Doctoral Fellow
June 1, 2014 – May 1, 2015
NYU Polytechnic School of Engineering
Research Assistant
September 1, 2011 – May 1, 2014
MBC Professional Electronics
Electronics Engineer
May 1, 2011 – April 1, 2013
Latchorzew, Poland
Warsaw University of Technology
Electronics Engineer
April 1, 2010 – August 1, 2011
Warszawa, woj. mazowieckie, Polska
Industrial Research for Automation and Measurements
Electronics Engineer
September 1, 2009 – June 1, 2011
Warszawa, woj. mazowieckie, Polska
IMPACT Automotive Technologies Sp. z o.o.
Electonics Engineer
October 1, 2008 – August 1, 2009
Pow. pruszkowski,oj. mazowieckie, Polska
Wireless Charger on EV Battary
January 1, 2014 – Present
The objective is to develop a high frequency(over 100k) 10kW wireless charger for electrical vehicle, with multiphase series resonant converter(SRC). My research in this project focus on modeling multi-phase SRC and design nonlinear feedback controller for both frequency and phase control, developing optimization control algorithm for efficiency maximization purpose.
Wireless Power Transfer Using Class E Inverters
January 1, 2014 – Present
• Design and development of Class E inverters for small and medium power, wireless power transfer. • Research on optimal control strategy for maximum efficiency of power transfer. • Designing schematics, PCB and Control design.
MS Thesis: Wireless dynamic charging
September 1, 2013 – Present
a) Designed and assembled universal multiphase resonant inverter platform b) Prepared schematics and PCB designs for power supply, gate drive, protection module, RS232, RS485 communication module, isolated amplifier, current amplifier and MCU of resonant inverter by applying Altium Designer c) Built water cooling system for DC programmable load with power up to 17.6 kW. d) Assembled PCBs by soldering, and tested PCBs applying scopes, fluke multimeters and Labview e) Assembled heat sink, enclosure, interfaces, cables of inverter via soldering, mechanical wrenching and screwing f) Programmed and implement FPGA (XC6SLX9) for switches control, communication with microprocessor, testbench and phase measurement in Xilinx with VHDL g) Programmed microcontroller (STM32) for generating PWM signal, ADC, DAC, current protection, voltage protection and implementation of control algorithm in C language
Cultural Fit Analysis
The candidate's background is heavily focused on electrical engineering, power electronics, and embedded systems, with a recent role in Deep Learning R&D. While the technical depth is significant, the target role of 'Data Analyst' represents a substantial pivot from their core expertise. There is no explicit experience or projects related to data analysis, statistical modeling, data visualization, or common data analysis tools (e.g., Python, R, SQL, Tableau, Power BI). This indicates a significant mismatch with the target role, suggesting a low cultural fit for a typical Data Analyst position without further evidence of relevant skills.
Soft Skills & Operational Fit
The candidate's extensive project and research descriptions indicate strong problem-solving abilities, a methodical approach to engineering challenges, and experience in leading technical initiatives. The detailed descriptions suggest good communication of technical concepts, although direct assessment of soft skills like teamwork or stress handling is not possible without psychometric test results or interview data.