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Machine Learning Engineer at Facebook
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Carnegie Mellon University
Master's degree, Intelligent Information Systems at LTI, School of Computer Science
January 1, 2014 – January 1, 2015
Indian Institute of Technology, Kharagpur
Dual Degree (B.Tech + M.Tech), Electrical Engineering (Spl. in Instrumentation Engineering)
January 1, 2007 – January 1, 2012
Machine Learning Engineer
July 1, 2020 – Present
Uber
Sr. ML/Data Scientist
October 1, 2018 – July 1, 2020
Uber
Data Scientist / Machine Learning Engineer
January 1, 2017 – September 1, 2018
Uber
Software Engineer
January 1, 2016 – January 1, 2017
Bloomberg LP
Machine Learning Intern
May 1, 2015 – August 1, 2015
New York
Amazon
Software Development Engineer
October 1, 2013 – June 1, 2014
Bengaluru, Karnataka, India
Microsoft Research India
Research Assistant
September 1, 2012 – June 1, 2013
Bangalore, India
Cultural Fit Analysis
The candidate has a strong background in Machine Learning and Data Science, with significant experience at large tech companies. While the target role is 'Backend Engineer', the candidate's recent experience is heavily skewed towards ML/AI. This might indicate a potential mismatch with a pure backend engineering role that does not involve ML infrastructure or services. The diversity of projects is within the ML/AI domain, which is a strength for ML roles but less so for general backend engineering. The candidate's experience level (12 years) is substantial.
Soft Skills & Operational Fit
The candidate's resume highlights roles involving building and deploying ML models, suggesting strong problem-solving and execution skills. Experience in conversational AI and payment risk implies an ability to work on complex, high-impact projects. However, specific soft skills like teamwork, leadership, or communication are not explicitly detailed in the provided data.