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Software Engineering Manager for Deep Learning Frameworks at NVIDIA
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
General Assembly
Other, Data Science Course
January 1, 2014 – January 1, 2014
University of Illinois Urbana-Champaign
MS, Electrical and Computer Engineering
January 1, 2002 – January 1, 2007
University of Illinois Urbana-Champaign
BS, Computer Engineering
January 1, 2000 – Present
NVIDIA
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Software Engineer for Pytorch nvFuser Team
February 1, 2020 – June 1, 2021
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Software Engineer for Deep Learning
September 1, 2019 – February 1, 2020
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Senior Deep Learning Architect
April 1, 2015 – September 1, 2019
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SM / CWD (GPU) Architect
December 1, 2012 – March 1, 2015
Advanced Micro Devices
Senior Design Engineer
April 1, 2010 – December 1, 2012
Advanced Micro Devices
Senior Design Engineer
May 1, 2007 – March 1, 2010
IBM
Microprocessor Performance Intern
May 1, 2006 – August 1, 2006
Austin, Texas Metropolitan Area
IBM
Microprocessor Performance intern
May 1, 2003 – August 1, 2003
Austin, Texas Metropolitan Area
International Business Machines
FPU Logic Design Engineer
September 1, 2000 – August 1, 2002
Austin, Texas Metropolitan Area
Cultural Fit Analysis
The candidate has a strong background in hardware and low-level software development, particularly in GPU and CPU architectures, which aligns well with a technical, performance-driven culture. Their experience spans multiple major tech companies (IBM, AMD, NVIDIA), indicating adaptability. However, the target role is 'FPGA Developer', and while there's a strong hardware background, direct FPGA development experience is not explicitly detailed in the resume, which could be a gap in cultural fit for a dedicated FPGA team.
Soft Skills & Operational Fit
The candidate's progression into engineering management roles at NVIDIA suggests strong leadership, project management, and team collaboration skills. Their detailed descriptions of complex technical optimizations indicate strong problem-solving and analytical abilities. The long tenure at NVIDIA and AMD points to operational stability and commitment.