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Distinguished Engineer: DL Software @ 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
Stanford University
Phd, Electrical Engineering
January 1, 2009 – January 1, 2013
Indian Institute of Technology, Bombay
BTech, MTech, Electrical Engineering, Microelectronics
January 1, 2004 – January 1, 2009
NVIDIA
Distinguished Engineer, Manager
January 1, 2024 – Present
Santa Clara, California, United States · On-site
Cruise
Senior Staff Machine Learning Engineer
February 1, 2022 – January 1, 2024
San Francisco Bay Area
Staff Silicon Architect and TLM
September 1, 2016 – February 1, 2022
San Francisco Bay Area
IBM Research
Research Staff Member
September 1, 2013 – September 1, 2016
Yorktown Heights, New York
IBM
Summer Research Intern
July 1, 2012 – September 1, 2012
Yorktown Heights, NY
imec
Visiting Scholar
June 1, 2011 – September 1, 2011
Leuven, Belgium
Rambus
Research and Technology Development Intern
June 1, 2010 – September 1, 2010
Stanford University
Graduate Student
September 1, 2009 – September 1, 2013
Cultural Fit Analysis
The candidate's experience is heavily focused on hardware, silicon architecture, and machine learning engineering, particularly in optimization and deployment. The target role is 'Data Analyst'. This represents a significant mismatch in core responsibilities and skill sets. While the candidate has a strong background in data-intensive fields (ML), the specific analytical and reporting aspects typically associated with a Data Analyst role are not explicitly demonstrated. The breadth of experience is high, but its direct relevance to a Data Analyst role is low.
Soft Skills & Operational Fit
The candidate's resume highlights leadership roles (Distinguished Engineer, Staff Silicon Architect and TLM, Senior Staff Machine Learning Engineer) indicating strong leadership and team management potential. The descriptions of building infrastructure for automating neural network development suggest problem-solving and innovation skills. However, without specific project details or interview data, it is difficult to assess communication, collaboration, and stress handling directly.