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Staff Software Engineer, Machine Learning at Google
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Staff Software Engineer, Machine Learning at Google. I did my masters at McGill University where i was working in the area of Deep Learning Techniques for Automatic Speech Recognition. In the past, I have done various pattern classification and machine learning related projects. I love to invent, to create innovative ML solutions to help organizations achieve their goals with minimum resources and help them bring those models to production.
McGill University
Master of Engineering (MEng) Thesis, Electrical Engineering
January 1, 2013 – January 1, 2016
IIT Bhubaneswar
Bachelor of Technology (BTech) Hons., Electrical Engineering
January 1, 2009 – January 1, 2013
Staff Software Engineer
May 1, 2025 – Present
Senior Software Engineer, Machine learning
April 1, 2022 – May 1, 2025
Software Engineer, Machine Learning
June 1, 2020 – April 1, 2022
North
Machine Learning Engineer
May 1, 2016 – June 1, 2020
Kitchener, Canada Area
Nuance Communications
Research Intern
May 1, 2014 – August 1, 2014
Montreal, Canada Area
McGill University
Research Assistant
September 1, 2013 – December 1, 2015
Montreal, Canada Area
Deutsche Telekom / T - Systems
Research Intern
May 1, 2012 – July 1, 2012
Indian Institute of Technology, Delhi
Research Intern
May 1, 2011 – July 1, 2011
Cultural Fit Analysis
The candidate has a strong background in research and development within machine learning, working for prominent tech companies. Their experience in building teams and contributing to investor demos suggests a proactive and collaborative mindset. However, the candidate's entire career trajectory is deeply rooted in Machine Learning Engineering and Software Engineering, with a focus on model development and deployment. The target role of 'Data Analyst' represents a significant pivot. While ML engineers possess strong analytical capabilities, the day-to-day responsibilities and typical skill sets (e.g., advanced SQL, dashboarding tools, business domain knowledge for data interpretation) for a dedicated Data Analyst role might not align perfectly with their demonstrated experience. This could indicate a potential mismatch in expectations or a need for significant re-skilling/re-focusing for the target role.
Soft Skills & Operational Fit
The candidate's experience at North highlights involvement in team building, mentoring, and investor demos, indicating strong communication and leadership skills. The description of implementing, evaluating, and deploying ML models suggests a practical, results-oriented approach. However, the target role is 'Data Analyst', which is a significant shift from their extensive Machine Learning Engineer/Software Engineer background. While analytical skills are inherent in ML, the specific operational fit for a pure Data Analyst role, which often involves more direct business intelligence, reporting, and SQL-heavy data manipulation, is not explicitly demonstrated.