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Research Scientist (ML) Manager at Meta
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I enjoy building teams and solving applied machine learning problems for consumer facing applications that touch billions of users.
Stanford University
Doctor of Philosophy (Ph.D.), Computer Science
January 1, 2012 – January 1, 2017
Indian Institute of Technology, Kanpur
Bachelor of Technology (B.Tech.), Computer Science
January 1, 2008 – January 1, 2012
Meta
Research Scientist Manager
September 1, 2023 – Present
Meta
Staff Research Scientist (Machine Learning Engineer)
March 1, 2018 – September 1, 2023
Trooly
Summer Intern
June 1, 2015 – August 1, 2015
Los Altos, California
Microsoft
Summer Research Intern (Microsoft Research)
July 1, 2013 – September 1, 2013
Redmond, Washington
Stanford University
PHD Candidate
September 1, 2012 – January 1, 2018
Microsoft
Summer Research Intern (Microsoft Research)
May 1, 2011 – July 1, 2011
Redmond, Washington
Stanford University
Summer Research Intern
May 1, 2010 – July 1, 2010
Indian Institute of Science (IISc)
Summer Research Intern
May 1, 2009 – August 1, 2009
India
Cultural Fit Analysis
The candidate's background is heavily skewed towards Machine Learning, Research, and Data Science roles, primarily within large tech companies and academia. While this demonstrates strong analytical and technical depth, the target role is 'Backend Engineer'. The resume does not explicitly detail experience in traditional backend engineering aspects such as API design, distributed systems architecture (beyond ML systems), specific programming languages (other than implied ML frameworks), or database management from a backend engineering perspective. This suggests a potential mismatch with a pure backend engineering role, though their ML systems experience could be transferable to backend roles focused on ML infrastructure.
Soft Skills & Operational Fit
The candidate's resume highlights leadership in ML teams and driving product-ML initiatives, suggesting strong collaboration and problem-solving skills. The descriptions of revenue impact and 0->1 automation tools indicate a results-oriented and innovative mindset. However, without specific psychometric test results or interview data, a detailed assessment of stress handling or team collaboration is not possible.